Multi-element thermal fluid generating system for heavy oil thermal recovery

Abstract

The invention provides a multi-element thermal fluid generation system for heavy oil thermal recovery, which relates to the field of heavy oil thermal recovery and is characterized by comprising a data acquisition module, an oil reservoir collaboration module, a dual-energy injection module and a conveying and injection module, the data acquisition module acquires oil reservoir temperature, pressure and other information through a sensor network and operates an energy balance model in combination with a data processing platform and an edge computing device to generate preliminary optimization parameters, and the oil reservoir collaboration module analyzes oil reservoir stability and recovery efficiency by using a deep learning prediction model. A final optimization parameter is generated through an optimization feedback unit, and the dual-energy injection module is guided to adjust the proportion, temperature and pressure of the hot fluid; the dual-energy injection module generates saturated steam and high-temperature thermal fluid through a trough type solar heat collector and a natural gas combustion device, and the saturated steam and the high-temperature thermal fluid are mixed through a dynamic thermal fluid regulation and control device and then conveyed to the conveying and injection module. The conveying and injecting module injects hot fluid into an oil reservoir and monitors the state in real time, closed-loop control is formed, heat energy distribution is optimized, and the thickened oil recovery efficiency is improved.

Description

Technical Field

[0001] The invention relates to the field of thermal recovery of heavy oil, in particular to a multi-component thermal fluid generating system for thermal recovery of heavy oil. Background Art

[0002] With the growth of global energy demand, the extraction of heavy oil and thick oil has gradually become an important issue in the oil industry. Traditional conventional extraction methods cannot effectively extract deep-layer heavy oil resources, resulting in low energy utilization efficiency. Heavy oil thermal recovery technology has become an effective extraction method. By injecting hot fluid to heat the oil reservoir, the fluidity of heavy oil is increased, thereby improving the recovery rate. In particular, the use of steam or hot water injection technology has been widely used in the extraction of heavy oil and has become one of the key technologies to improve recovery in the oil industry.

[0003] Existing heavy oil thermal recovery technologies mainly include steam displacement, hot water displacement and other thermal fluid injection methods. Steam displacement technology uses steam to inject into the oil reservoir to reduce its viscosity by heating the heavy oil, thereby promoting its flow. However, the steam injection method has high energy consumption and large heat loss, especially during the injection and transportation process, the efficiency is low. Other thermal fluid injection methods also have similar problems such as high energy consumption and low thermal efficiency, and fail to achieve efficient use of energy.

[0004] The main problems faced by existing technologies are serious energy losses during the generation and transportation of thermal fluids, and the difficulty in real-time regulation of the injection process. It is impossible to efficiently and accurately adjust the parameters of the thermal fluids, which restricts the production effect. Therefore, there is an urgent need for a technical solution that can improve the efficiency of thermal fluid injection and achieve precise regulation to optimize the heavy oil thermal recovery process. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the problems of high energy consumption, serious heat loss and lack of real-time dynamic control capability of reservoir conditions in the thermal fluid generation and transportation process in the prior art, the present invention provides a multi-element thermal fluid generation system for heavy oil thermal recovery, so as to achieve efficient optimization and precise control of the heavy oil thermal recovery process and solve the shortcomings of the prior art in terms of heat energy distribution and operational flexibility.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-element thermal fluid generation system for heavy oil thermal recovery, comprising: the data acquisition module collects the temperature, pressure, permeability and fracture information of the oil reservoir in real time through a sensor network, monitors the state parameters of the thermal fluid, and transmits the data to a data processing platform; the data processing platform generates input parameters for use by an edge computing device in combination; the edge computing device runs an energy balance model and an environmental prediction algorithm to generate preliminary optimization parameters, and transmits them to the oil reservoir collaboration module;

[0009] The reservoir collaboration module predicts the impact of injected thermal fluid on reservoir structural stability and recovery efficiency through a deep learning prediction model, combined with real-time reservoir data and historical injection records, and integrates edge computing results through an optimization feedback unit to generate final optimization parameters, which are then transmitted to the dual-energy injection module and the transport and injection module;

[0010] The dual-energy injection module utilizes a trough solar collector and a swirl combustion chamber to generate saturated steam and high-temperature and high-pressure thermal fluid respectively, adjusts the mixing ratio, output temperature and pressure parameters of the two thermal fluids in real time through a nonlinear energy balance model, and transmits the optimized mixed thermal fluid to the delivery and injection module;

[0011] The thermal fluid transportation and injection module injects the mixed thermal fluid of the dual-energy injection module into the oil reservoir through a high-pressure transportation pipeline and a dynamic flow regulating valve, and monitors the transportation status in real time through a temperature and pressure monitoring device, and dynamically adjusts the flow and pressure according to the feedback signal to ensure the high efficiency and stability of heavy oil thermal recovery.

[0012] Preferably, the data acquisition module collects multiple key reservoir parameters in real time, including the temperature, pressure, permeability and crack expansion of the reservoir, through a sensor network deployed in the reservoir area. Each sensor is equipped with a high-precision temperature and pressure probe, the permeability is measured by a micro flowmeter, and the crack expansion information is obtained by an acoustic wave reflection method. These sensors transmit real-time data to the data processing platform to ensure low latency and high stability of data transmission. At the same time, the sensor adopts an anti-interference design, can adapt to high temperature and high pressure environments, and has corrosion resistance. The data acquisition platform supports two-way communication, updates data in real time and feeds back sensor status information. Data is transmitted through a wireless communication protocol and has high fault tolerance. It will automatically recover when data is lost. All collected reservoir parameter data is processed by a denoising algorithm and transmitted to the data processing platform to generate raw data that meets the analysis standards. The collected reservoir parameter data is combined with historical records to complete data optimization, filtering and normalization processing, providing clear and high-quality data support for subsequent edge computing devices, and generating input parameters for calculating the energy balance model and the environmental prediction model.

[0013] Preferably, the edge computing device realizes accurate energy demand prediction and injection scheme optimization by running the energy balance model and the environmental prediction model. The core formula of the energy balance model is: Q balance =Q input -Q output , where Q input is the input energy of solar energy and natural gas thermal fluid, Q output It is the heat energy demand for injection into the reservoir; through real-time monitoring of input and output heat, the device can adaptively adjust the output parameters to ensure thermal energy balance. The environmental prediction model uses Markov chain to predict the future state of the system, combining temperature, pressure and reservoir changes to optimize the injection time and flow rate of the thermal fluid. By combining the output results of the two models, the edge computing device generates preliminary optimization parameters in real time, including key parameters such as the mixing ratio, temperature, and pressure of the thermal fluid; the final result is transmitted to the reservoir collaboration module through a high-bandwidth data interface.

[0014] Preferably, the deep learning prediction model adopts a multi-layer neural network architecture combining a convolutional neural network and a long short-term memory network to process inputs from a sensor network and historical injection data. The convolutional neural network layer is responsible for extracting spatial features of reservoir data, such as temperature gradients, pressure changes, and fracture extensions, while the LSTM layer processes time series data to capture the long-term effects of injected thermal fluids on reservoir structural stability and recovery efficiency. The model structure is: y=f(CNN(x1),(LSTM(x2))+b), where x1 is the spatial feature input, x2 is the time series data, f is a function that fuses the convolution and LSTM layers, and b is the deviation term, and y is the predicted value of the final output recovery rate or structural stability; during the training process, the deep learning model uses labeled historical data for supervised learning, such as past reservoir recovery rates and injection conditions, and is optimized by minimizing the mean square error (MSE) loss function. The Adam optimization algorithm is used to update the weights. After the training is completed, the deep learning model can predict the long-term impact of thermal fluid injection on the reservoir based on the input real-time reservoir data, and generate corresponding optimization parameters for further processing by the reservoir collaboration module. At the same time, the accuracy of the model is evaluated by cross-validation and hold-out methods to ensure the high reliability of the prediction results.

[0015] Preferably, the trough type solar collector captures solar radiation energy through the combined design of a high-reflectivity reflector and a heat absorbing tube. The reflectivity of the reflector is as high as 95%, which can effectively focus sunlight onto the surface of the heat absorbing tube. The heat absorbing tube is made of copper-based composite material and filled with softened water. The tube wall is coated with black chrome to improve the heat absorption efficiency. The softened water in the heat absorbing tube is transported to the dynamic thermal fluid control device through a high-pressure steam pipe to achieve low-loss transmission of saturated steam. After passing through the control device, the saturated steam is mixed with natural gas to generate a thermal fluid to adjust the optimal temperature and pressure for reservoir injection.

[0016] Preferably, the natural gas ultra-high pressure combustion vaporization device utilizes a swirl combustion chamber and a jet atomization device to efficiently burn and mix natural gas and softened water. The swirl combustion chamber adopts a multi-layer swirl design to cause the gas to rotate at high speed in the chamber to enhance the mixing effect. The core of the efficient combustion chamber is to improve the completeness of combustion by optimizing the airflow path in the combustion zone. The jet atomization device distributes softened water evenly to the combustion area through a high-speed rotating nozzle. The water droplets have a uniform diameter and are widely distributed. Through full contact with the high-temperature fuel gas, a high-temperature and high-pressure thermal fluid is generated. The thermal fluid is transmitted to the dynamic thermal fluid control device through a high-pressure output pipeline. The temperature and pressure during the combustion process are monitored in real time by precision sensors, and the combustion rate and gas mixing ratio are automatically adjusted to maintain combustion stability.

[0017] Preferably, the dynamic thermal fluid control device calculates the mixing ratio of the two thermal fluids through a nonlinear energy balance model. The nonlinear energy balance model dynamically adjusts the mixing ratio using a nonlinear optimization method based on the final optimization parameters of the reservoir feedback and the temperature, pressure and flow data of the real-time thermal fluid. The specific formula is: Where R represents the proportion of steam, Q 蒸汽 and Q 热流体 They represent the heat input of steam and high-temperature and high-pressure thermal fluid respectively. The proportional control valve is accurately adjusted according to the nonlinear optimization result, and can respond to changes within milliseconds to control the output flow and pressure. The temperature and pressure control unit monitors the state of the mixed thermal fluid in real time through an integrated high-precision sensor, and further adjusts the output parameters of the mixed thermal fluid based on the monitoring data to ensure that the output temperature, pressure and flow meet the optimization requirements of heavy oil thermal recovery and adapt to changes in complex working conditions.

[0018] Preferably, the delivery and injection module injects the mixed thermal fluid into the reservoir through a high-pressure delivery pipeline, and accurately controls the flow rate through a dynamic flow control valve; the temperature and pressure monitoring device is installed at the end of the pipeline, monitors the temperature, pressure and flow rate of the injected fluid in real time, and then transmits the data back to the acquisition module for processing in real time, and adjusts the injection flow rate and pressure based on the real-time feedback to ensure the stability and efficiency of the injection process. The system adopts a two-way feedback mechanism to adjust the injection conditions of the thermal fluid in real time according to the reaction of the reservoir and the change of the fluid state to ensure the uniform distribution of the thermal fluid and the high efficiency of continuous injection; the temperature and pressure monitoring device monitors the flow rate, temperature and pressure data of the reservoir injection process in real time through a high-precision sensor, transmits the data to the data acquisition module through a wireless communication module for processing, and compares it with the historical data to detect whether there is any abnormal fluctuation or mismatch; the data processing platform filters and corrects the collected temperature and pressure data through an algorithm to ensure the accuracy of the sensor data, and then generates optimized control instructions; under the two-way feedback mechanism of the system, when the temperature or pressure exceeds the set range, the system will automatically adjust the opening of the flow control valve, change the flow rate or pressure of the thermal fluid in time to restore the optimal injection condition, and the specific adjustment method is calculated by the formula: Among them, △Q is the flow change after adjustment, K is the proportional constant, P desired and P actual are set values ​​and actual monitoring values ​​respectively; the flow control valve responds to the adjustment signal, quickly changes the flow output, and maintains the balance of thermal fluid injection in the reservoir.

[0019] Preferably, the nonlinear energy balance model dynamically calculates the mixing ratio and injection conditions of the output thermal fluid according to the feedback parameters of the reservoir and the real-time monitored thermal fluid temperature, pressure, and flow data. Through the nonlinear optimization algorithm, the model objective is to minimize the error of the following formula: min(|Q 需求 -∑ i Q i | ) , where Q 需求 is the thermal energy demand of the reservoir under specific conditions, ∑ i Q i It is the heat contribution of each thermal fluid source, such as solar steam and natural gas thermal fluid. The optimization solution of this objective function is carried out through Newton iteration method to ensure that the system can calculate the optimal thermal fluid ratio in the shortest time to meet the recovery needs of the oil reservoir. The nonlinear algorithm generates precise instructions for the control system of the regulating valve, adjusts the proportional valve and the temperature and pressure control unit, so that the final output flow and pressure strictly meet the set standards.

[0020] Preferably, the data processing platform processes and optimizes the real-time data collected by the sensor, removes errors in sensor collection through the Kalman filter denoising algorithm, and further improves the accuracy of the data. The processed data is used to extract key features through the principal component analysis (PCA) method. The PCA method performs eigenvalue decomposition on the data matrix and selects the features in the direction of maximum variance for dimensionality reduction, thereby optimizing the utilization of computing resources. The processed data is transmitted to the edge computing device for further running the energy balance model and the environmental prediction model; it also supports data storage and historical data query to ensure data traceability and system optimization in long-term operation. The key features are temperature, pressure, permeability and crack information.

[0021] (III) Beneficial effects

[0022] The present invention provides a multi-element thermal fluid generation system for heavy oil thermal recovery, which has the following beneficial effects:

[0023] 1. The present invention has good real-time feedback and adjustment capabilities, can accurately control the injection process of thermal fluid, monitor the temperature, pressure, permeability and crack information of the reservoir in real time through the data acquisition module, and accurately control the flow and pressure through the dynamic flow control valve and temperature and pressure monitoring device. The system can dynamically adjust the injection parameters according to the actual reaction of the reservoir and the change of the thermal fluid state. This closed-loop feedback mechanism ensures that the thermal fluid is evenly injected into the reservoir, thereby improving the uniformity of the distribution of the thermal fluid in the reservoir, avoiding the generation of overheating or cold zones, maximizing the recovery efficiency of heavy oil, and reducing energy consumption and costs. In addition, the system's optimization feedback module analyzes the structural stability and recovery efficiency of the reservoir through a deep learning prediction model, further optimizes the injection plan, and ensures that the thermal recovery process is carried out under optimal conditions.

[0024] 2. The present invention integrates a variety of high-efficiency energy utilization technologies to achieve efficient generation and precise regulation of thermal fluids, significantly reducing system energy consumption. The dual-energy injection module maximizes the use of solar energy and natural gas energy through a combination of a trough solar collector and a natural gas ultra-high pressure combustion vaporization device to generate the required thermal fluid. The mixing ratio, temperature and pressure of the thermal fluid are dynamically adjusted through the optimization adjustment of the nonlinear energy balance model. The system can respond to and adjust the injection conditions of the thermal fluid in real time according to the needs of the reservoir to avoid energy waste. At the same time, the edge computing device combines real-time monitoring data with the energy balance model to accurately calculate the energy requirements for injecting thermal fluids, making the distribution and utilization of thermal energy more efficient, thereby reducing excessive energy consumption and improving resource utilization. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The embodiment of the present invention provides a multi-element thermal fluid generation system for heavy oil thermal recovery, which realizes efficient optimization and precise control of the heavy oil thermal recovery process by integrating a data acquisition module, a reservoir coordination module, a dual-energy injection module, and a transportation and injection module.

[0027] First, as the information input end of the system, the data acquisition module collects key reservoir parameters in real time through the sensor network deployed in the reservoir area, including the temperature, pressure, permeability and crack expansion of the reservoir. These sensors are equipped with high-precision temperature and pressure probes, the permeability is measured by a micro flow meter, and the crack expansion information is obtained by the acoustic reflection method. The sensor transmits real-time data to the data processing platform to ensure low latency and high stability of data transmission. The sensor adopts an anti-interference design and can adapt to high temperature and high pressure environments and has corrosion resistance. The data acquisition platform supports two-way communication, updates data in real time and feedbacks sensor status information. Data is transmitted through a wireless communication protocol with high fault tolerance and will automatically recover when data is lost. All collected reservoir parameter data is processed by a denoising algorithm and transmitted to the data processing platform to generate raw data that meets the analysis standards. The collected reservoir parameter data is combined with historical records to complete data optimization, filtering and normalization processing, providing clear and high-quality data support for subsequent edge computing devices.

[0028] Next, the edge computing device runs the energy balance model and environmental prediction algorithm to achieve accurate energy demand prediction and injection plan optimization; through real-time monitoring of input and output heat, the device can adaptively adjust the output parameters to ensure thermal energy balance. The environmental prediction model uses Markov chains to predict the future state of the system, combining temperature, pressure and reservoir changes to optimize the injection time and flow rate of the thermal fluid. By combining the output results of the two models, the edge computing device generates preliminary optimization parameters in real time, including key parameters such as the mixing ratio, temperature, and pressure of the thermal fluid; the final result is transmitted to the reservoir collaboration module through a high-bandwidth data interface.

[0029] The reservoir collaboration module uses a deep learning prediction model to combine real-time reservoir data with historical injection records to predict the impact of injected thermal fluids on reservoir structural stability and recovery efficiency. The deep learning prediction model uses a multi-layer neural network architecture that combines convolutional neural networks and long short-term memory networks to process inputs from sensor networks and historical injection data. The convolutional neural network layer is responsible for extracting spatial features of reservoir data, such as temperature gradients, pressure changes, and fracture expansion, while the LSTM layer processes time series data to capture the long-term impact of injected thermal fluids on reservoir structural stability and recovery efficiency. During the training process, the deep learning model uses labeled historical data for supervised learning, such as past reservoir recovery rates and injection conditions, and optimizes by minimizing the mean square error (MSE) loss function. The Adam optimization algorithm is used to update weights. After training, the deep learning model can predict the long-term impact of thermal fluid injection on the reservoir based on the input real-time reservoir data and generate corresponding optimization parameters for further processing by the reservoir collaboration module.

[0030] Then the dual-energy injection module uses the trough solar collector and the swirl combustion chamber to generate saturated steam and high-temperature and high-pressure thermal fluid respectively. The trough solar collector captures solar energy through high-reflectivity mirrors and heat absorption tubes to generate saturated steam, while the natural gas combustion vaporization device generates high-temperature and high-pressure thermal fluid through the swirl combustion chamber and the injection atomization device; these two thermal fluids are mixed in the dynamic thermal fluid control device, and the mixing ratio, output temperature and pressure are dynamically adjusted according to the final optimization parameters transmitted by the reservoir coordination module to meet the thermal recovery needs of heavy oil.

[0031] Finally, the transportation and injection module injects the mixed thermal fluid of the dual-energy injection module into the reservoir through a high-pressure transportation pipeline and a dynamic flow control valve, and monitors the transportation status in real time through a temperature and pressure monitoring device, dynamically adjusts the flow and pressure according to the feedback signal to ensure the high efficiency and stability of heavy oil thermal recovery; the temperature and pressure monitoring device is installed at the end of the pipeline, monitors the temperature, pressure and flow of the injected fluid in real time, and then transmits the data back to the data acquisition module for processing in real time, and adjusts the injection flow and pressure according to the real-time feedback to ensure the stability and efficiency of the injection process.

[0032] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-element thermal fluid generation system for heavy oil thermal recovery, comprising a data acquisition module, a reservoir coordination module, a dual-energy injection module and a transport and injection module, characterized in that ； The data acquisition module collects the temperature, pressure, permeability and fracture information of the oil reservoir in real time through the sensor network, monitors the state parameters of the thermal fluid, transmits the data to the data processing platform, and generates analysis results for use by the edge computing device; the edge computing device runs the energy balance model and environmental prediction algorithm, generates preliminary optimization parameters, and transmits them to the oil reservoir collaboration module; The reservoir collaboration module predicts the impact of injected thermal fluid on reservoir structural stability and recovery efficiency through a deep learning prediction model, combined with real-time reservoir data and historical injection records, and integrates edge computing results through an optimization feedback unit to generate final optimization parameters, which are then transmitted to the dual-energy injection module and the transport and injection module; The dual-energy injection module utilizes a trough solar collector and a swirl combustion chamber to generate saturated steam and high-temperature and high-pressure thermal fluid respectively, adjusts the mixing ratio, output temperature and pressure parameters of the two thermal fluids in real time through a nonlinear energy balance model, and transmits the optimized mixed thermal fluid to the delivery and injection module; The thermal fluid transportation and injection module injects the mixed thermal fluid of the dual-energy injection module into the oil reservoir through a high-pressure transportation pipeline and a dynamic flow regulating valve, and monitors the transportation status in real time through a temperature and pressure monitoring device, and dynamically adjusts the flow and pressure according to the feedback signal to ensure the high efficiency and stability of heavy oil thermal recovery.

2. A multi-component thermal fluid generating system for heavy oil thermal recovery according to claim 1, characterized in that: The data acquisition module includes a multi-parameter acquisition unit, a data processing platform and an edge computing device. The multi-parameter acquisition unit collects reservoir temperature, pressure, permeability and crack information in real time, as well as the status data of the thermal fluid fed back by the transportation and injection module; the data processing platform analyzes the changes in thermal fluid and reservoir conditions in combination with historical records and real-time data to obtain analysis results; the edge computing device runs an energy balance model and an environmental prediction model according to the analysis results to generate preliminary optimization parameters, including mixing ratio, temperature and pressure, and transmits the preliminary optimization parameters to the optimization feedback unit. The analysis results include an in-depth analysis of reservoir status and thermal fluid changes, including trend analysis, change rules and predicted energy demand and optimization parameters of temperature, pressure, permeability, crack extension and thermal fluid status data.

3. The multi-element thermal fluid generating system for heavy oil thermal recovery according to claim 1, characterized in that: The reservoir collaboration module includes a sensor network, a deep learning prediction model and an optimization feedback unit. The sensor network collects reservoir temperature, pressure, permeability and fracture extension information in real time. The deep learning prediction model uses a multi-layer neural network architecture combining a convolutional neural network and a long short-term memory network, and combines the sensor network data and historical injection parameters to predict the long-term impact of thermal fluid injection on reservoir structural stability and recovery efficiency. The optimization feedback unit integrates the prediction model results with the preliminary optimization parameters generated by the edge computing device to generate final optimization parameters to adjust the thermal fluid injection scheme.

4. The multi-element thermal fluid generating system for heavy oil thermal recovery according to claim 1, characterized in that: The dual-energy injection module includes a trough solar collector and a natural gas ultra-high pressure combustion vaporization device. The trough solar collector captures solar energy through high-reflectivity reflectors and heat absorption tubes to generate saturated steam. The natural gas combustion vaporization device generates high-temperature and high-pressure thermal fluid through a swirl combustion chamber and a jet atomization device. The two thermal fluids are mixed through a dynamic thermal fluid control device. The mixing ratio, output temperature and pressure are dynamically adjusted according to the final optimization parameters transmitted by the reservoir coordination module to meet the thermal recovery needs of heavy oil.

5. The multi-element thermal fluid generating system for heavy oil thermal recovery according to claim 1, characterized in that: The transportation and injection module includes a high-pressure transportation pipeline, a dynamic flow regulating valve and a temperature and pressure monitoring device; the high-pressure transportation pipeline transports the mixed thermal fluid generated by the dual-energy injection module to the reservoir injection device; the dynamic flow regulating valve dynamically adjusts the injection flow according to the final optimization parameters transmitted by the reservoir collaborative module; the temperature and pressure monitoring device monitors the flow, temperature and pressure during the transportation process in real time, and transmits the monitoring data to the data acquisition module to achieve dynamic optimization and closed-loop control.

6. The multi-element thermal fluid generating system for heavy oil thermal recovery according to claim 1, characterized in that: The trough solar collector uses high-reflectivity mirrors and heat-absorbing tubes to capture solar radiation energy. The heat-absorbing tubes are filled with softened water and use high-thermal-conductivity copper-based composite materials to optimize heat transfer efficiency. The inner wall of the heat-absorbing tube is coated with black chrome to improve heat absorption efficiency. It is connected to a dynamic thermal fluid control device through a high-pressure steam pipe to ensure low-loss transmission of saturated steam.

7. The multi-element thermal fluid generating system for heavy oil thermal recovery according to claim 1, characterized in that: The natural gas ultra-high pressure combustion vaporization device adopts a swirl combustion chamber and a jet atomization device to efficiently burn and mix natural gas and softened water; the swirl combustion chamber adopts a multi-layer swirl structure to ensure the sufficiency of combustion; the jet atomization device evenly distributes softened water in the combustion area and generates high-temperature and high-pressure thermal fluid through high-temperature combustion; the high-temperature and high-pressure thermal fluid is transported to the dynamic thermal fluid control device through a high-pressure output pipeline.

8. The multi-element thermal fluid generating system for heavy oil thermal recovery according to claim 1, characterized in that: The dynamic thermal fluid control device includes a nonlinear energy balance model, a proportional control valve and a temperature and pressure control unit; the nonlinear energy balance model is based on the final optimization parameters fed back by the reservoir collaborative module, combined with the real-time monitored state data of the thermal fluid, including temperature, pressure and flow data, and dynamically adjusts the mixing ratio of saturated steam and high-temperature and high-pressure thermal fluid through a nonlinear algorithm to achieve a dynamic balance of thermal energy distribution; the proportional control valve is driven by a high-precision servo to support precise control of the flow and pressure of the fluid according to the output result of the nonlinear energy balance model; the temperature and pressure control unit monitors the state of the mixed thermal fluid in real time through an integrated high-precision sensor and an intelligent controller, and further corrects the control process based on the monitoring results to ensure that the output temperature, pressure and flow meet the optimization requirements of heavy oil thermal recovery and adapt to changes in complex working conditions.