A dynamic evaluation method for reservoir helium resources based on real-time data

Abstract

The present invention relates to the technical field of helium resource evaluation, and specifically discloses a dynamic evaluation method for reservoir helium resources based on real-time data, comprising: obtaining precise helium concentration data; performing three-dimensional modeling and finite element stress field coupling analysis on the precise helium concentration data to generate helium distribution migration data; processing the helium distribution migration data using a deep learning model to obtain helium resource evolution data; performing dynamic recovery rate evaluation and path optimization processing on the helium resource evolution data under multiphase flow constraints to generate a final mining plan; performing strategy self-correction processing on the final mining plan to ultimately output a dynamic evaluation and intelligent control plan; the reservoir stress simulation and multiphase flow optimization of the present invention can predict and avoid potential mining risks, ensure safe mining of the reservoir, and through efficient resource recovery and precise resource evaluation, the system helps to extend the service life of the reservoir.

Description

Technical Field

[0001] The present invention belongs to the technical field of helium resource evaluation, and in particular relates to a dynamic evaluation method for reservoir helium resources based on real-time data. Background Art

[0002] Existing reservoir helium resource evaluation methods primarily rely on static geological exploration data and traditional resource assessment models. These methods typically include geological surveys, core analysis, and reservoir simulation to determine reservoir structure, physical properties, and fluid characteristics. However, these methods have significant limitations, particularly when faced with complex and changing reservoir environments and dynamically changing helium resources.

[0003] Traditional resource assessment models are often based on simplified assumptions and idealized conditions, making it difficult to accurately simulate the actual flow and distribution of helium in reservoirs. These models often overlook the impact of reservoir stress and multiphase flow on helium distribution and migration, resulting in significant errors in assessment results and a lack of support from real-time data and the ability to dynamically adjust. During the actual production process, the reservoir state and helium distribution will change, but traditional assessment methods are unable to capture these changes in a timely manner, nor can they dynamically adjust and optimize based on real-time data. As a result, production plans are often based on outdated data and information, failing to effectively address reservoir uncertainties.

[0004] To this end, the inventors proposed a dynamic evaluation method for reservoir helium resources based on real-time data to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for dynamic evaluation of reservoir helium resources based on real-time data to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for dynamic evaluation of reservoir helium resources based on real-time data, comprising:

[0008] Acquire helium concentration data in the reservoir, perform real-time monitoring and adaptive filtering and denoising on the helium concentration in the reservoir to obtain accurate helium concentration data;

[0009] Performing three-dimensional modeling and finite element stress field coupling analysis on the precise helium concentration data, and generating helium distribution and migration data in combination with real-time formation pressure and temperature data;

[0010] Based on the helium distribution and migration data, a deep learning model is used to extract spatiotemporal features and perform dynamic prediction processing to obtain helium resource evolution data;

[0011] Performing dynamic recovery rate evaluation and path optimization processing on the helium resource evolution data under multiphase flow constraints to obtain a final mining plan;

[0012] The final mining plan is subjected to strategy self-correction processing to obtain a dynamic evaluation and intelligent control plan.

[0013] Preferably, the adaptive filtering denoising algorithm is used to remove noise and errors from the gas concentration monitoring equipment; Kalman filtering is used as a denoising algorithm to predict and correct noise components in the real-time data stream, and the Kalman filtering formula is:

[0014] ;

[0015] in, is the state estimate at the current moment; is the current observation value; is the observation matrix, which describes the observation process of the system and converts the system state into observation values; is the Kalman gain, which is used to determine the system's fusion weight of the observed value and the predicted value;

[0016] By filtering, the random noise of the measuring equipment can be removed to obtain more accurate helium concentration data; the reliability of the real-time monitoring system can be improved, and accurate data input can be provided for subsequent dynamic evaluation.

[0017] Preferably, the finite element stress field coupling analysis is used to simulate reservoir stress and gas distribution. Based on the geological structure of the reservoir, the changes in stress, pressure, and temperature factors are simulated, and the spatial distribution and migration trend of helium are inferred. The formula of the finite element stress field coupling analysis is:

[0018] ;

[0019] in, is the formation stress tensor, which describes the stress state of each point in the reservoir;

[0020] J is the gas flow, which describes the flow behavior of helium in the reservoir and is affected by temperature and pressure factors;

[0021] Q is the source term (such as heat source, gas flow, etc.), which describes the release and adsorption process of helium;

[0022] Indicates the change of reservoir stress over time, reflecting the stress changes caused by the production process, gas flow or other external factors;

[0023] Used to describe the flow trend of helium in the reservoir, the divergence operator calculates the outflow or inflow of gas, helping to understand the gas distribution and the dynamic state of the reservoir;

[0024] Finite element stress field coupling analysis can simulate the stress changes in reservoirs at different production stages, predict the distribution and flow of helium, and optimize recovery strategies.

[0025] Through dynamic simulation of stress and gas distribution, potential mining risks can be identified in advance, reducing resource waste or wellhead damage caused by unstable gas distribution;

[0026] Finite element analysis is used to simulate the stress state of the reservoir and thus infer the distribution of helium. Combining 3D reservoir modeling with real-time data, dynamic simulation of reservoir helium distribution is performed using factors such as formation pressure, stress changes, and temperature.

[0027] Preferably, the deep learning model is a convolutional neural network (CNN) fused with a long short-term memory network (LSTM), and the deep learning model is used to process time series data (such as helium concentration changes, temperature, and pressure) and predict the dynamic changes of reservoir helium resources;

[0028] Wherein, the convolutional neural network is used to extract spatial features from helium concentration time series data;

[0029] The convolutional neural network formula is:

[0030] ;

[0031] Among them, ht is the output feature of the convolutional layer;

[0032] W is the convolution kernel weight, which extracts features from the input data and learns the spatial characteristics of the reservoir helium concentration;

[0033] b is the bias;

[0034] xt is the input data (helium concentration data or other parameters);

[0035] The long short-term memory network is used to capture the trend of helium concentration changing over time. The long short-term memory network formula is:

[0036] ;

[0037] Wf and Wi are the gating weights of the long short-term memory network: they are used to control the size of information flow and capture important dynamic information in the time series.

[0038] For forecasting output: Based on the combination of convolutional neural network and long short-term memory network, the future changes of reservoir helium resources are predicted;

[0039] Combining CNN and LSTM can not only make short-term predictions of helium resources, but also model long-term change trends and dynamically adjust mining strategies.

[0040] The real-time dynamic adjustment capability of this model significantly improves the accuracy and response speed of resource assessment.

[0041] Preferably, the multiphase flow constraint is used to optimize the flow path of helium in the reservoir, and a particle swarm optimization algorithm (PSO) is used to update the particle velocity at time t+1, and the optimal mining path and recovery plan are found through iteration, thereby improving the recovery rate of resources;

[0042] The formula of the particle swarm optimization algorithm is:

[0043] ;

[0044] Where vi(t) is the particle velocity at time t, which is used to adjust the velocity of the helium flow path and is updated based on the current state and the historical best position;

[0045] xi is the position of the particle, representing the helium flow path or harvesting strategy;

[0046] pbesti is the best historical position of the particle;

[0047] gbest is the global best position, representing the optimal helium recovery path;

[0048] w is the inertia weight;

[0049] c1, c2 are acceleration constants;

[0050] r1, r2 are random numbers;

[0051] The fitness of each particle's new position, i.e., the new scheme, is evaluated. The fitness function combines the helium flow characteristics, reservoir state, and recovery efficiency under multiphase flow constraints, reflecting the pros and cons of the scheme in terms of helium recovery efficiency and risk control.

[0052] If the fitness of the new solution is better than the best solution in the history of the particle, the historical optimal solution of the particle is updated; if its fitness is better than the global optimal solution, the global optimal solution is updated;

[0053] Repeat the above iterative process until the preset maximum number of iterations is reached or the fitness converges;

[0054] Finally, the particle position corresponding to the global optimal solution gbest is the optimal helium mining path and recovery plan under the multiphase flow constraint;

[0055] The particle swarm optimization algorithm can quickly find the optimized helium recovery path, reduce resource waste, and improve the recovery rate. The algorithm adaptively adjusts the recovery path and can dynamically optimize the mining plan based on real-time data, improving the economic benefits and sustainability of the entire mining process.

[0056] Preferably, the formula for the strategy self-correction process is:

[0057] ;

[0058] Where u(t) represents the control strategy at time t, such as mining parameters;

[0059] ytarget(t) represents the expected target, such as target recovery rate or resource volume;

[0060] yactual(t) represents the actual data obtained by real-time monitoring;

[0061] α is the adjustment factor;

[0062] The above formula describes the process of achieving dynamic evaluation and intelligent regulation by continuously correcting the control strategy through real-time error feedback, that is, the difference between the target and the actual value.

[0063] Preferably, the helium concentration data is obtained by real-time monitoring of the helium concentration in the reservoir using a high-sensitivity spectrometer;

[0064] The precise helium concentration data is obtained by removing noise and correcting errors from the helium concentration data using an adaptive filtering and denoising algorithm.

[0065] Preferably, the dynamic recovery rate evaluation and path optimization processing are achieved based on real-time reservoir data and a simulated gas-liquid phase conversion model.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The reservoir stress simulation and multiphase flow optimization in the present invention can predict and avoid potential mining risks, ensuring the safe mining of the reservoir; at the same time, by improving the efficiency of resource recovery, unnecessary mining costs and environmental impacts are reduced; real-time data visualization and automated report generation can improve management efficiency, reduce manual intervention, ensure that decisions are based on accurate real-time data, and provide managers with real-time feedback and early warning mechanisms to improve work efficiency and accuracy; through efficient resource recovery and accurate resource assessment, the system helps to extend the service life of the reservoir, reduce the risk of over-exploitation, and promote environmental protection and sustainable mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1The present invention is a flow chart of a method for dynamic evaluation of reservoir helium resources based on real-time data. DETAILED DESCRIPTION

[0069] The following will provide a clear and complete description of 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.

[0070] Example 1:

[0071] See also Figure 1 As shown, the embodiment of the present application provides a method for dynamic evaluation of reservoir helium resources based on real-time data, including:

[0072] In step S1, helium concentration data in the reservoir is obtained, and the helium concentration in the reservoir is monitored in real time and subjected to adaptive filtering and denoising processing to obtain accurate helium concentration data;

[0073] In the above steps, the helium concentration data is obtained by real-time monitoring of the helium concentration in the reservoir using a high-sensitivity spectrum analyzer;

[0074] The precise helium concentration data is obtained by removing noise and correcting errors of the helium concentration data using an adaptive filtering and denoising algorithm;

[0075] Real-time monitoring of helium concentration in reservoirs is performed using highly sensitive spectrometers, such as infrared spectroscopy, to generate accurate helium concentration data. Real-time monitoring of helium concentration is the basis for reservoir helium resource assessment.

[0076] Gas analysis is performed using an infrared spectrometer. The instrument can be installed in reservoir monitoring wells to ensure that the sampling points are representative of the helium distribution in the reservoir;

[0077] Data is transmitted to the data processing platform via wireless communication (such as LoRa);

[0078] Apply Kalman Filter to smooth the collected real-time data and make adaptive adjustments to cope with external environmental changes and equipment noise;

[0079] The filtered data is passed to the subsequent stage for further analysis;

[0080] The adaptive filtering denoising algorithm is used to remove noise and errors from the gas concentration monitoring equipment. Kalman filtering is used as a denoising algorithm to predict and correct noise components in the real-time data stream. The Kalman filtering formula is:

[0081] ;

[0082] in, is the state estimate at the current moment; is the current observation value; is the observation matrix, which describes the observation process of the system and converts the system state into observation values; is the Kalman gain, which is used to determine the system's fusion weight of the observed value and the predicted value;

[0083] By filtering, the random noise of the measuring equipment can be removed to obtain more accurate helium concentration data; the reliability of the real-time monitoring system can be improved, and accurate data input can be provided for subsequent dynamic evaluation.

[0084] In step S2, three-dimensional modeling and finite element stress field coupling analysis are performed on the precise helium concentration data, and helium distribution and migration data are generated in combination with real-time formation pressure / temperature data;

[0085] Finite Element Analysis (FEM): The finite element method is used to perform three-dimensional reservoir modeling and simulate the stress distribution of the reservoir and the dynamic changes of helium.

[0086] Formation pressure and temperature monitoring: Combined with real-time pressure and temperature data, the stress changes in the reservoir are calculated and the migration path of helium in the reservoir is inferred.

[0087] Use seismic data and drilling data to generate a three-dimensional reservoir model, and apply finite element analysis software such as ANSYS to simulate the stress state of the reservoir at different production stages.

[0088] Combined with real-time monitoring data, the simulation results are adjusted in real time to predict the flow and distribution of helium;

[0089] The finite element stress field coupling analysis is used to simulate reservoir stress and gas distribution. Based on the geological structure of the reservoir, it simulates the changes in stress, pressure, and temperature factors, and infers the spatial distribution and migration trend of helium. The formula for the finite element stress field coupling analysis is:

[0090] ;

[0091] in, is the formation stress tensor, which describes the stress state of each point in the reservoir;

[0092] J is the gas flow, which describes the flow behavior of helium in the reservoir and is affected by temperature and pressure factors;

[0093] Q is the source term (such as heat source, gas flow, etc.), which describes the release and adsorption process of helium;

[0094] Indicates the change of reservoir stress over time, reflecting the stress changes caused by the production process, gas flow or other external factors;

[0095] Used to describe the flow trend of helium in the reservoir, the divergence operator calculates the outflow or inflow of gas, helping to understand the gas distribution and the dynamic state of the reservoir;

[0096] Finite element stress field coupling analysis can simulate the stress changes in reservoirs at different production stages, predict the distribution and flow of helium, and optimize recovery strategies.

[0097] Through dynamic simulation of stress and gas distribution, potential mining risks can be identified in advance, reducing resource waste or wellhead damage caused by unstable gas distribution;

[0098] Finite element analysis is used to simulate the stress state of the reservoir and thus infer the distribution of helium. Combining 3D reservoir modeling with real-time data, dynamic simulation of reservoir helium distribution is performed using factors such as formation pressure, stress changes, and temperature.

[0099] Based on the helium distribution and migration data, a deep learning model is used to extract spatiotemporal features and perform dynamic prediction processing to obtain helium resource evolution data;

[0100] Deep learning prediction uses deep learning technology to combine real-time monitoring data with historical data to predict the dynamic changes in helium resources in the reservoir and adjust the recovery strategy based on the prediction results.

[0101] Deep learning training: Use historical data to train the model and make dynamic predictions through real-time data input.

[0102] Build a deep learning model integrating CNN and LSTM, and use frameworks such as TensorFlow and Keras for model training and application.

[0103] Combining real-time monitoring data with historical mining data, a deep learning model is used to predict helium resources and generate dynamically changing forecast results.

[0104] The deep learning model is a convolutional neural network (CNN) fused with a long short-term memory network (LSTM). The deep learning model is used to process time series data (such as changes in helium concentration, temperature, and pressure) and predict the dynamic changes of reservoir helium resources;

[0105] Wherein, the convolutional neural network is used to extract spatial features from helium concentration time series data;

[0106] The convolutional neural network formula is:

[0107] ;

[0108] Among them, ht is the output feature of the convolutional layer;

[0109] W is the convolution kernel weight, which extracts features from the input data and learns the spatial characteristics of the reservoir helium concentration;

[0110] b is the bias;

[0111] xt is the input data (helium concentration data or other parameters);

[0112] The long short-term memory network is used to capture the trend of helium concentration changing over time. The long short-term memory network formula is:

[0113] ;

[0114] Wf and Wi are the gating weights of the long short-term memory network: they are used to control the size of information flow and capture important dynamic information in the time series.

[0115] For forecasting output: Based on the combination of convolutional neural network and long short-term memory network, the future changes of reservoir helium resources are predicted;

[0116] Combining CNN and LSTM can not only make short-term predictions of helium resources, but also model long-term change trends and dynamically adjust mining strategies.

[0117] The real-time dynamic adjustment capability of this model significantly improves the accuracy and response speed of resource assessment

[0118] In step S3, the helium resource evolution data is subjected to dynamic recovery rate evaluation and path optimization under multiphase flow constraints to generate a final mining plan; the dynamic recovery rate evaluation and path optimization are achieved by using a gas-liquid phase conversion model based on real-time reservoir data and simulation;

[0119] Based on the helium resource change results output by the deep learning model and combined with real-time reservoir data, dynamic recovery rate evaluation is performed and a dynamic helium resource evaluation report is automatically generated;

[0120] Dynamic recovery rate assessment: Evaluate the current reservoir recovery rate based on changes in helium concentration and predicted resource volume.

[0121] Dynamic report generation: Automatically generate reports on current resource reserves, mining progress, and expected recovery rates to help decision makers understand current reservoir conditions.

[0122] Use real-time data inputs, such as pressure and concentration, combined with the output of deep learning models to perform recovery rate assessment.

[0123] Generate reports based on the evaluation results using data visualization tools and provide real-time feedback to operators;

[0124] Based on real-time reservoir data and gas-liquid phase conversion models, the extraction path and recovery plan of reservoir helium are optimized to improve recovery efficiency and reduce risks.

[0125] Multiphase flow simulation: Use gas-liquid phase transition models to simulate the interaction between helium and other gases or liquids in the reservoir to optimize production plans.

[0126] Mining path optimization: Based on real-time data and simulation results, the mining path of helium is optimized to ensure efficient recovery.

[0127] Use multiphase flow simulation software (such as CMG, ECLIPSE) combined with real-time data to adjust the mining path.

[0128] Optimization algorithms (such as particle swarm optimization and genetic algorithms) can be used to adjust the helium mining path to improve recovery rate and efficiency;

[0129] The multiphase flow constraint is used to optimize the flow path of helium in the reservoir. The particle swarm optimization (PSO) algorithm is used to update the particle velocity at time t+1. The optimal mining path and recovery plan are found through iteration, thereby improving the resource recovery rate.

[0130] The formula of the particle swarm optimization algorithm is:

[0131] ;

[0132] Where vi(t) is the particle velocity at time t, which is used to adjust the velocity of the helium flow path and is updated based on the current state and the historical best position;

[0133] xi is the position of the particle, representing the helium flow path or harvesting strategy;

[0134] pbesti is the best historical position of the particle;

[0135] gbest is the global best position, representing the optimal helium recovery path;

[0136] w is the inertia weight;

[0137] c1, c2 are acceleration constants;

[0138] r1, r2 are random numbers;

[0139] The fitness of each particle's new position, i.e., the new scheme, is evaluated. The fitness function combines the helium flow characteristics, reservoir state, and recovery efficiency under multiphase flow constraints, reflecting the pros and cons of the scheme in terms of helium recovery efficiency and risk control.

[0140] If the fitness of the new solution is better than the best solution in the history of the particle, the historical optimal solution of the particle is updated; if its fitness is better than the global optimal solution, the global optimal solution is updated;

[0141] Repeat the above iterative process until the preset maximum number of iterations is reached or the fitness converges;

[0142] Finally, the particle position corresponding to the global optimal solution gbest is the optimal helium mining path and recovery plan under the multiphase flow constraint;

[0143] The particle swarm optimization algorithm can quickly find the optimized helium recovery path, reduce resource waste, and improve the recovery rate. The algorithm adaptively adjusts the recovery path and can dynamically optimize the mining plan based on real-time data, improving the economic benefits and sustainability of the entire mining process.

[0144] Preferably, the formula for the strategy self-correction process is:

[0145] ;

[0146] Where u(t) represents the control strategy at time t, such as mining parameters;

[0147] ytarget(t) represents the expected target, such as target recovery rate or resource volume;

[0148] yactual(t) represents the actual data obtained by real-time monitoring;

[0149] α is the adjustment factor;

[0150] The above formula describes the process of achieving dynamic evaluation and intelligent regulation by continuously correcting the control strategy through real-time error feedback, that is, the difference between the target and the actual value.

[0151] The final mining plan is subjected to strategy self-correction processing, and a dynamic evaluation and intelligent control plan is finally output.

[0152] As can be seen above, reservoir stress simulation and multiphase flow optimization can predict and avoid potential mining risks, ensuring safe reservoir extraction. Furthermore, by improving resource recovery efficiency, unnecessary mining costs and environmental impacts are reduced. Real-time data visualization and automated report generation improve management efficiency, reduce manual intervention, and ensure that decisions are based on accurate real-time data. They also provide managers with real-time feedback and early warning mechanisms, improving work efficiency and accuracy. Through efficient resource recovery and accurate resource assessment, the system helps extend the life of the reservoir, reduce the risk of overexploitation, and promote environmental protection and sustainable mining. Example 2:

[0153] Dynamic evaluation and prediction of reservoir helium concentration based on real-time data:

[0154] Application scenario: In a medium-sized helium reservoir, real-time monitoring of helium concentration, formation pressure, and temperature data is combined with a deep learning model (CNN + LSTM) to dynamically evaluate and predict helium resources.

[0155] Specifically, helium concentration data (monitoring frequency: once an hour):

[0156] Initial concentration: 2.1% (initial reservoir concentration);

[0157] Concentration range: 2.1% - 4.8%;

[0158] Monitoring frequency: once an hour, collection period: 24 hours;

[0159] Formation pressure data (unit: MPa):

[0160] Initial pressure: 12.0 MPa;

[0161] Pressure range: 12.0 MPa - 15.5 MPa;

[0162] Monitoring frequency: once an hour, collection period: 24 hours;

[0163] Reservoir temperature data (unit: °C):

[0164] Initial temperature: 35°C;

[0165] Temperature range: 35℃ - 38℃;

[0166] Monitoring frequency: once an hour, collection period: 24 hours;

[0167] Training dataset:

[0168] Historical data: Real-time monitoring data for the past six months, including helium concentration, formation pressure, temperature data, and recovery rate.

[0169] Calculation process:

[0170] Data preprocessing: Kalman filtering is used to denoise the collected helium concentration, formation pressure, and temperature data to eliminate interference caused by external environmental factors or equipment noise.

[0171] The original helium concentration data are: 2.1%, 2.3%, 2.5%, ..., 4.8% (collected once every hour).

[0172] The helium concentration data after using Kalman filtering might be smoothed to: 2.3%, 2.5%, 2.6%, ..., 4.6%.

[0173] Feature extraction (Convolutional Neural Network CNN):

[0174] Input data: 24 hours of helium concentration, formation pressure and temperature data.

[0175] The features extracted by the CNN layer include information such as local temporal changes and fluctuation range, for example, the maximum, minimum and mean values ​​of concentration changes.

[0176] Deep learning model (CNN + LSTM) training and prediction:

[0177] The features processed by the CNN are input into the LSTM layer. The LSTM model learns the dynamic changes in helium concentration over time. Assume that the LSTM output predicts a value of 4.3% for the next 24 hours.

[0178] Helium resource assessment: Assuming the reservoir volume is 100,000 cubic meters and the current concentration is 4.3%, the current helium resource in the reservoir can be calculated as:

[0179] Helium resources = reservoir volume × helium concentration

[0180] Helium resources = 100,000 m 3 ×0.043=4300 m 3

[0181] Therefore, the current helium resource in the reservoir is approximately 4,300 cubic meters.

[0182] Effect and significance:

[0183] Improved prediction accuracy: The deep learning model has a prediction error of less than 5%, which is approximately 15% higher than traditional statistical methods (such as linear regression).

[0184] Real-time dynamic evaluation: Using real-time data and dynamic predictions, we can timely understand the changes in helium resources in the reservoir, avoiding the traditional static evaluation method based on historical data.

[0185] Mining decision optimization: Through real-time helium concentration prediction and resource estimation, mining plans can be optimized, recovery rates can be increased, and mining costs can be reduced.

[0186] From the above, we can see that through the comprehensive use of real-time data monitoring, deep learning, optimization algorithms and automated processing technologies, the system not only improves the efficiency of helium resource extraction, reduces costs and risks, but also enhances the intelligence and automation level of the decision-making process, and promotes the modernization and sustainable development of helium resource management.

[0187] Example 3:

[0188] Helium production path optimization based on multiphase flow optimization:

[0189] Application scenario: In a large-scale helium reservoir, by real-time monitoring of helium concentration, pressure, and temperature, combined with a multiphase flow optimization algorithm (PSO), the helium recovery path is optimized to improve the recovery rate and recovery efficiency.

[0190] Further, basic reservoir data:

[0191] Reservoir volume: 500,000 cubic meters;

[0192] Initial helium concentration: 2.5%;

[0193] Initial pressure: 10.0 MPa;

[0194] Initial temperature: 30°C;

[0195] Real-time monitoring data:

[0196] Helium concentration: 2.5% - 3.8% (collected once every hour);

[0197] Pressure: 10.0 MPa - 12.5 MPa (collected once every hour);

[0198] Temperature: 30℃ - 32℃ (collected once every hour);

[0199] Particle Swarm Optimization (PSO) algorithm parameters:

[0200] Particle group size: 50;

[0201] Maximum number of iterations: 100;

[0202] Inertia weight w=0.9;

[0203] Acceleration constants c1=1.5, c2=1.5;

[0204] Random numbers r1, r2∈[0,1];

[0205] Calculation process:

[0206] Particle swarm initialization: Initialize 50 particles, each of which represents a possible mining path. The particle's position indicates the choice of mining well, and its velocity indicates the magnitude of the path adjustment.

[0207] Objective function design: The goal is to maximize the recovery rate. Considering the real-time data of helium concentration, pressure, and temperature, the objective function is designed:

[0208] ;

[0209] in:

[0210] Pi is the pressure of the i-th production well,

[0211] Ci is the helium concentration of the i-th production well,

[0212] Ti is the temperature of the i-th production well.

[0213] Assume there are three production wells, and their pressure, helium concentration, and temperature data are as follows:

[0214] Production well 1: pressure P1 = 10.5 MPa, helium concentration C1 = 2.8%, temperature T1 = 31°C

[0215] Production well 2: pressure P2 = 11.0 MPa, helium concentration C2 = 3.2%, temperature T2 = 30°C

[0216] Production well 3: pressure P3 = 12.0 MPa, helium concentration C3 = 3.5%, temperature T3 = 32°C

[0217] Substitute the data to calculate the objective function value:

[0218] ;

[0219] Particle swarm optimization process: Particle swarm optimization iteratively updates particle positions and velocities to maximize the objective function.

[0220] Furthermore, after 100 iterations, the particle swarm finally found the optimal path, and its objective function value increased to 1.25.

[0221] Mining Path Optimization: The optimal path prioritizes mining in areas with high pressure and helium concentration, adjusting mining strategies based on real-time monitoring data. This optimized path increased the recovery rate by approximately 12%.

[0222] From the above, we can see that by optimizing the mining path through the particle swarm optimization algorithm, the recovery rate is increased by 12%, which can make helium recovery more efficient and reduce energy consumption and resource waste.

[0223] Through dynamic feedback of real-time pressure, concentration and temperature data, the production path is optimized and the risk of reservoir rupture or wellhead instability caused by excessive or insufficient local pressure is reduced.

[0224] The particle swarm optimization algorithm can dynamically adjust the mining strategy according to real-time data and adapt to reservoir changes, and has strong flexibility and adaptability.

[0225] As can be seen above, the combination of real-time monitoring, data preprocessing, deep learning, and simulation optimization technologies can provide accurate, real-time helium resource assessment results, providing strong support for resource management and mining decision-making. The application of dynamic monitoring and predictive models can significantly improve the scientific nature and efficiency of mining plans. By adjusting strategies in real time and optimizing mining paths, the recovery rate of helium can be increased and waste can be reduced.

[0226] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic evaluation of reservoir helium resources based on real-time data, characterized in that: include: Acquire helium concentration data in the reservoir, perform real-time monitoring and adaptive filtering and denoising on the helium concentration in the reservoir to obtain accurate helium concentration data; Performing three-dimensional modeling and finite element stress field coupling analysis on the precise helium concentration data, and generating helium distribution and migration data in combination with real-time formation pressure and temperature data; Based on the helium distribution and migration data, a deep learning model is used to extract spatiotemporal features and perform dynamic prediction processing to obtain helium resource evolution data; Performing dynamic recovery rate evaluation and path optimization processing on the helium resource evolution data under multiphase flow constraints to obtain a final mining plan; Performing strategy self-correction processing on the final mining plan to obtain a dynamic evaluation and intelligent control plan; The finite element stress field coupling analysis is used to simulate reservoir stress and gas distribution. Based on the geological structure of the reservoir, it simulates the changes in stress, pressure, and temperature factors and infers the spatial distribution and migration trend of helium. The multiphase flow constraint is used to optimize the flow path of helium in the reservoir. The particle swarm optimization algorithm is used to update the particle velocity at time t+1, and the optimal mining path and recovery plan are found through iteration.

2. The method for dynamic evaluation of reservoir helium resources based on real-time data according to claim 1, characterized in that: The adaptive filtering denoising algorithm is used to remove noise from the gas concentration monitoring equipment; Kalman filtering is used as a denoising algorithm to predict and correct noise components in real-time data streams. The Kalman filtering formula is: x k|k =x k|k-1 +K k ·(z k -H k x k|k-1 ) Among them, x k|k is the state estimate at the current moment; z k is the current observation value; H k is the observation matrix, which is used to describe the observation process of the system and convert the system state into observation values; K k is the Kalman gain, which is used to determine the system's fusion weight of observed values ​​and predicted values.

3. The method for dynamic evaluation of reservoir helium resources based on real-time data according to claim 1, characterized in that: The formula for the finite element stress field coupling analysis is: Where σ is the formation stress tensor, which describes the stress state of each point in the reservoir; J is the gas flow, which describes the flow behavior of helium in the reservoir and is affected by temperature and pressure factors; Q is the source term, describing the release and adsorption process of helium; Indicates the change of reservoir stress over time, reflecting the stress changes caused by the mining process and gas flow; Used to describe the flow trend of helium in a reservoir, the divergence operator calculates the outflow or inflow of gas, helping to understand gas distribution and the dynamic state of the reservoir.

4. The method for dynamic evaluation of reservoir helium resources based on real-time data according to claim 1, characterized in that: The deep learning model is a convolutional neural network fused with a long short-term memory network, which is used to process time series data and predict the dynamic changes of reservoir helium resources; Wherein, the convolutional neural network is used to extract spatial features from helium concentration time series data; The convolutional neural network formula is: ht=RELU(W·xt+b) Where ht is the output feature of the convolution layer; W is the convolution kernel weight, which extracts features from the input data and learns the spatial characteristics of the reservoir helium concentration; b is the bias; xt is the input data; The long short-term memory network is used to capture the trend of helium concentration changing over time. The long short-term memory network formula is: h′t=sigmoid(Wf·ht-1+Wi·xt) Wf, Wi are the gating weights of the long short-term memory network: they are used to control the size of information flow and capture important dynamic information in the time series; h′t is the predicted output: based on the combination of convolutional neural network and long short-term memory network, the future changes of reservoir helium resources are predicted.

5. The method for dynamic evaluation of reservoir helium resources based on real-time data according to claim 1, characterized in that: The formula of the particle swarm optimization algorithm is: vi(t+1)=w·vi(t)+c1·r1·(pbesti-xi)+c2·r2·(gbest-xi) in, vi(t) is the velocity of the particle at time t, which is used to adjust the velocity of the helium flow path and is updated based on the current state and the historical best position; xi is the position of the particle, representing the helium flow path or harvesting strategy; pbesti is the best historical position of the particle; gbest is the global best position, representing the optimal helium recovery path; w is the inertia weight; c1, c2 are acceleration constants; r1, r2 are random numbers.

6. The method for dynamic evaluation of reservoir helium resources based on real-time data according to claim 1, characterized in that: The formula for the strategy self-correction process is: u(t+1)=u(t)+α·[ytarget(t)-yactual(t)] Where u(t) represents the control strategy at time t; ytarget(t) represents the expected target; yactual(t) represents the actual data obtained by real-time monitoring; α is the adjustment factor.

7. The method for dynamic evaluation of reservoir helium resources based on real-time data according to claim 1, characterized in that: The helium concentration data is obtained by real-time monitoring of the helium concentration in the reservoir using a high-sensitivity spectrum analyzer; The precise helium concentration data is obtained by performing noise removal and error correction on the helium concentration data using an adaptive filtering and denoising algorithm.

8. The method for dynamic evaluation of reservoir helium resources based on real-time data according to claim 1, characterized in that: The dynamic recovery rate evaluation and path optimization process are achieved based on real-time reservoir data and a simulated gas-liquid phase conversion model.

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