Underground heat exchange comprehensive optimization method and system for energy cascade utilization

Abstract

The present application belongs to the technical field of geothermal energy development and utilization, and relates to a geothermal energy downhole heat exchange comprehensive optimization method and system for energy cascade utilization. The method comprises the following steps: obtaining thermal energy monitoring data of a fluid inlet and thermal flow channel fluid monitoring data, and performing feature extraction to obtain multi-stage thermal energy output features; obtaining a thermal energy use demand information set of an energy utilization object; dividing a set of energy utilization objects according to the multi-stage thermal energy output features and the thermal energy use demand information set, and outputting a set of multi-stage energy utilization objects; establishing a connection relationship between the set of multi-stage energy utilization objects and the fluid inlet and independent heat exchange flow channels, and performing geothermal energy downhole heat exchange cascade optimization transmission based on the connection relationship. The present application solves the problem that the existing geothermal energy downhole heat exchange adopts single-depth single-flow channel design, cannot collect different depth thermal energy difference cascades, and relies on experience for thermal energy distribution, thereby leading to the problems of low utilization of high-temperature thermal energy, idling of low-temperature thermal energy, and low utilization efficiency.

Description

Technical Field

[0001] This invention belongs to the field of geothermal energy development and utilization technology, and relates to a comprehensive optimization method and system for downhole heat exchange of geothermal energy oriented towards energy cascade utilization. Background Technology

[0002] Geothermal energy, with its abundant reserves, stable supply, and relatively small environmental impact, has become an important alternative to traditional fossil fuels. Compared to the intermittency and volatility of renewable energy sources such as solar and wind power, geothermal energy can provide a continuous baseload energy source, giving it unique application value. Among the various development methods for geothermal energy, downhole heat exchange technology has become an important development path because it does not require the extraction of underground fluids, avoiding problems such as geothermal water reinjection, corrosion and scaling, and potential geological environmental impacts. This technology extracts geothermal energy by laying closed heat exchange pipelines in the well, allowing the circulating working fluid to exchange heat with the rock and soil mass underground.

[0003] However, existing downhole heat exchange technologies still face significant efficiency bottlenecks in large-scale applications. Currently, most mainstream downhole heat exchange schemes employ a single-depth, single-channel design, meaning heat exchange is carried out using a single channel structure across the entire depth of the heat exchange well. Due to the prevalent geothermal gradient, the temperature of the rock and soil at different depths in the well varies significantly, typically increasing with depth. This single-depth extraction method cannot adapt to the natural temperature distribution of geothermal resources, resulting in underutilization of high-temperature heat energy and idle low-temperature heat energy.

[0004] Furthermore, existing downhole heat exchange systems generally lack dynamic optimization control mechanisms. The systems typically operate under fixed conditions and cannot adaptively adjust based on real-time load demands from the user side, dynamic changes in the geothermal field (such as localized geothermal temperature drops caused by long-term heat extraction), and energy consumption characteristics in different seasons and time periods. Summary of the Invention

[0005] To address the technical problems of existing geothermal well downhole heat exchange technologies, which employ a single-depth, single-channel design, failing to capture heat energy at different depths in a tiered manner and relying on experience for heat energy distribution, resulting in underutilization of high-temperature heat energy and idle low-temperature heat energy, leading to overall low utilization efficiency, this invention provides a comprehensive optimization method and system for geothermal well downhole heat exchange oriented towards tiered energy utilization. To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a comprehensive optimization method for downhole heat exchange of geothermal energy for energy cascade utilization, comprising the following steps: Historical thermal energy monitoring data and historical hot flow channel fluid monitoring data of multiple fluid inlets are acquired; the fluid inlets are the fluid inlets of the segmented coaxial casing of the geothermal well, the multiple fluid inlets correspond to multiple downhole stepped depths, and the segmented coaxial casing also includes multiple independent heat exchange channels; Based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data, thermal energy output characteristics are extracted to obtain multi-level thermal energy output characteristics. Acquire a set of thermal energy usage demand information from multiple energy utilization objects; Based on the multi-level thermal energy output characteristics, the thermal energy usage demand information set is analyzed, and the energy utilization object set is divided to output a multi-level energy utilization object set. Establish the connection relationship between the multi-level energy utilization object set and the multiple fluid inlets and multiple independent heat exchange channels, and optimize the downhole heat exchange transmission of geothermal energy based on the connection relationship.

[0006] Preferably, the step of extracting thermal output features based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data to obtain multi-level thermal energy output features includes: A time-series feature analysis is performed on the historical thermal energy monitoring data and the historical hot flow channel fluid monitoring data to extract the thermal energy output characteristics corresponding to each fluid inlet; the thermal energy output characteristics include at least the maximum output thermal energy, the average output thermal energy, the thermal energy stability index, and the thermal recovery characteristics. The heat output characteristics are normalized to obtain standardized multi-level heat output characteristics.

[0007] Preferably, the step of analyzing the heat energy usage demand information set based on the multi-level heat energy output characteristics and dividing the energy utilization object set to output a multi-level energy utilization object set includes: Feature extraction is performed on the set of thermal energy demand information to obtain multi-level thermal energy demand characteristics; the multi-level thermal energy demand characteristics include at least the demanded thermal energy temperature, average heat load, thermal energy stability and thermal regulation flexibility. Based on the multi-level thermal energy output characteristics and the multi-level thermal energy demand characteristics, an optimization is performed using a preset objective function to output a multi-level energy utilization object set; the multi-level energy utilization object set is a mapping table of connection relationships between the multiple fluid inlets and the energy utilization objects; wherein, the objective function is used to maximize the comprehensive benefits of thermal energy output utilization rate and thermal energy consumption value weight.

[0008] Preferably, the optimization using a preset objective function includes: Obtain multiple optimization constraints; the multiple optimization constraints include at least temperature matching constraints, load matching constraints, stability matching constraints, and adjustment flexibility matching constraints; Under the premise of satisfying multiple optimization constraints, the objective function is optimized based on the multi-level thermal energy output characteristics and the multi-level thermal energy demand characteristics to obtain the optimal set of multi-level energy utilization objects.

[0009] Preferably, the temperature matching constraint, load matching constraint, stability matching constraint, and adjustment elasticity matching constraint are used to constrain the values ​​corresponding to the thermal energy output characteristics of the fluid inlet to meet the requirements of the energy utilization object based on the thermal energy output characteristics.

[0010] Preferably, the step of optimizing the objective function based on the multi-level heat energy output characteristics and the multi-level heat energy demand characteristics, under the premise of satisfying multiple optimization constraints, includes: Define output variables, which include the distribution relationship between the fluid inlet and the energy utilization object and the corresponding distributed thermal power; Under the premise of satisfying multiple optimization constraints, analyze the multi-stage heat energy output characteristics and the multi-stage heat energy demand characteristics to determine the solution space; An iterative optimization algorithm is used to search within the solution space, including: Select the first output variable that makes the objective function achieve its current optimal value; The solution space is iteratively updated based on the first combination of output variables to obtain the second output variable; The objective function value corresponding to the first output variable is compared with the objective function value corresponding to the second output variable until the preset convergence condition is met, and a multi-level energy utilization object set is output.

[0011] Preferably, the step of optimizing the downhole heat exchange and transmission of geothermal energy based on the connection relationship includes: Based on the allocation relationship and the allocated heat power, the valve opening degree of multiple valves is controlled by mapping the timing nodes and control opening parameters to obtain the valve opening degree sequence. The geothermal energy downhole heat exchange cascade optimization is performed according to the valve opening sequence. The multiple fluid inlets are controlled by multiple valves.

[0012] Secondly, the present invention provides a comprehensive optimization system for downhole heat exchange of geothermal energy for energy cascade utilization, comprising: Historical data acquisition module: used to acquire historical thermal energy monitoring data and historical hot flow channel fluid monitoring data of multiple fluid inlets; the fluid inlets are the fluid inlets of the segmented coaxial casing downhole of the geothermal well, the multiple fluid inlets correspond to multiple downhole stepped depths, and the segmented coaxial casing also includes multiple independent heat exchange channels; Output feature extraction module: used to extract thermal energy output features based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data, and obtain multi-level thermal energy output features; Demand information extraction module: used to obtain a set of thermal energy usage demand information from multiple energy utilization objects; The object output module is used to analyze the set of thermal energy usage demand information based on the multi-level thermal energy output characteristics, divide the set of energy utilization objects, and output a multi-level set of energy utilization objects. Connection relationship establishment module: used to establish the connection relationship between the multi-level energy utilization object set and the multiple fluid inlets and multiple independent heat exchange channels, and to perform geothermal energy downhole heat exchange cascade optimization transmission based on the connection relationship.

[0013] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of a comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention achieves precise quantification of the supply capacity of geothermal resources at different depths by acquiring historical monitoring data at multiple depths and extracting standardized thermal energy output characteristics. Simultaneously, it constructs a complete supply and demand data foundation by collecting demand information from energy utilization targets and transforming it into matchable demand characteristics. Furthermore, it utilizes optimization algorithms to intelligently match and classify supply and demand characteristics under multiple constraints, outputting a clear set of multi-level energy utilization targets. Finally, based on this set, it establishes and executes optimized connections and transmission control with downhole fluid inlets and independent flow channels. This invention effectively solves the resource mismatch problem of high-temperature underutilization and low-temperature idleness, significantly improving the overall utilization efficiency and economy of downhole geothermal heat exchange. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the system of the present invention.

[0018] The module includes: 11. Historical data acquisition module; 12. Output feature extraction module; 13. Demand information extraction module; 14. Utilize object output module; 15. Connection relationship establishment module. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0024] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0025] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 like Figure 1 As shown in the figure, this embodiment provides a comprehensive optimization method for downhole heat exchange of geothermal energy for energy cascade utilization. The method includes: Step S100: Obtain historical thermal energy monitoring data for multiple fluid inlets and historical hot flow channel fluid monitoring data.

[0026] Specifically, historical thermal energy monitoring data refers to historical data related to geothermal energy collected from multiple fluid inlets over a past period, including real-time temperature, instantaneous thermal power, and daily cumulative thermal energy at the inlet. Historical heat flow channel fluid monitoring data refers to historical records of the state parameters of the heat exchange medium flowing in multiple independent heat exchange channels over a past period, mainly including fluid flow rate, velocity, inlet and outlet pressure difference, and specific heat capacity of the medium.

[0027] Specifically, thermal monitoring equipment, such as platinum resistance temperature sensors, is installed at each fluid inlet to monitor the temperature of the geothermal medium at the inlet; heat flow meters are used to record instantaneous thermal power. The heat flow meters synchronously record the thermal power during that period. Fluid parameter monitoring tools, such as electromagnetic flow meters, are deployed at the inlet and outlet positions of each independent heat exchange channel to measure fluid flow rate; pressure transmitters are used to monitor the pressure difference between the inlet and outlet. Through an industrial data acquisition device, such as a PLC control system, all sensor data is transmitted in real time to a local database for classification and storage, forming a continuous historical dataset.

[0028] By collecting historical thermal energy data and hot flow channel fluid data from multiple fluid inlets over a long period, the geothermal output patterns and dynamic characteristics of the flow channel fluid at different cascade depths can be comprehensively captured. This provides real and continuous data support for subsequent feature extraction, avoiding feature analysis biases caused by short-term data, and thus ensuring the scientific validity and accuracy of subsequent cascade optimization and matching.

[0029] The fluid inlet is the fluid inlet of the segmented coaxial casing downhole of the geothermal well. The multiple fluid inlets correspond to multiple downhole stepped depths. The segmented coaxial casing also includes multiple independent heat exchange channels. Specifically, segmented coaxial casing is a concentric casing structure designed in segments along the well depth direction. It consists of an inner tube, an outer tube, and an intermediate partition layer, with an overall coaxial layout, adaptable to the structural strength and heat exchange requirements of different downhole environments. The fluid inlet is the interface on the casing used to introduce the heat exchange medium. After entering the casing through the inlet, the medium absorbs geothermal energy. Independent heat exchange channels are non-interconnected channels formed by partition structures inside the casing. The heat exchange medium in each channel flows independently, avoiding the mixing of media at different temperatures that would affect heat exchange efficiency.

[0030] Specifically, the downhole temperature distribution in the target area is explored using tools such as geothermal thermometers and logging instruments to determine the tiered depth. Based on the tiered depth, a segmented coaxial casing structure is designed, and multiple independent heat exchange channels are formed by annular partition plates between the inner and outer pipes.

[0031] By using a segmented coaxial casing with multiple inlets and multiple flow channels, geothermal energy at different depth levels can be collected simultaneously, avoiding the limitation of traditional single-depth casings that can only collect heat energy at a single temperature. This lays the hardware foundation for matching different utilization needs according to energy level, and significantly improves the comprehensiveness of geothermal energy collection and the potential for cascade utilization.

[0032] Step S200: Extract thermal energy output features based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data to obtain multi-level thermal energy output features.

[0033] Specifically, the multi-level thermal energy output characteristics correspond to multiple fluid inlets at different downhole depths. Each inlet forms an independent set of thermal energy output characteristics. For example, the high-temperature, medium-temperature, and low-temperature stages each have a set of characteristics, reflecting the differentiated characteristics of geothermal output at different depths.

[0034] Specifically, historical data is cleaned to remove outliers caused by sensor malfunctions and fill in missing data. Based on the cleaned data, characteristic indicators are calculated: for each fluid inlet, the maximum output thermal energy, average output thermal energy, thermal stability index, and thermal recovery characteristics are calculated; the characteristic values ​​are mapped using a normalization method to form a multi-level thermal energy output characteristic with unified dimensions.

[0035] By extracting multi-level thermal energy output characteristics, historical monitoring data is transformed into intuitive and comparable quantitative indicators, clearly presenting a profile of the supply capacity of geothermal resources at each depth. This provides a standardized basis for the precise classification and matching of subsequent energy utilization targets, avoiding the blindness of direct matching based on raw data and significantly improving the scientific nature of cascade utilization schemes.

[0036] Step S300: Obtain a set of thermal energy usage demand information for multiple energy utilization objects.

[0037] Specifically, the term "energy utilization objects" refers to the general term for all end-use entities that consume geothermal energy, covering different fields such as residential, industrial, and agricultural sectors. Examples include residential community heating systems, industrial production heat equipment, agricultural greenhouse temperature control systems, and geothermal power generation units. The set of thermal energy demand information refers to the combination of key parameters extracted from the set of energy utilization objects, reflecting the core thermal energy needs of each object. This typically includes the required temperature range, average heat load, heat load fluctuation frequency, thermal stability requirements, and thermal regulation response speed.

[0038] Specifically, an industrial IoT platform is used to establish a connection channel with a collection of energy utilization objects, enabling real-time data interaction with each object. Demand information is extracted using a dual-mode approach combining automatic data collection and manual data entry: for parameters that can be monitored in real time, such as the real-time heat load of the heating system and the required temperature of the generator set, data is collected through temperature sensors and heat flow meters deployed at the object's end to obtain real-time parameters; for non-real-time but critical demands, such as the seasonal heat load fluctuation range of agricultural greenhouses and the annual heat consumption duration of industrial equipment, a structured form system is used; and an SQL Server database is used to classify and integrate the collected, dispersed demand parameters, format them, and form a complete set of heat energy usage demand information.

[0039] This step, by establishing a standardized process, transforms the previously scattered and vague geothermal energy demand into quantifiable and structured parameters, providing a clear demand-side profile for subsequent precise supply and demand matching. Simultaneously, the combination of real-time data collection and manual data entry ensures both the timeliness of demand data and coverage of non-real-time key information, avoiding geothermal energy mismatch caused by ambiguous demand and laying a demand-side data foundation for improving the efficiency of geothermal energy cascade utilization.

[0040] Step S400: Analyze the set of thermal energy usage demand information based on the multi-level thermal energy output characteristics, divide the set of energy utilization objects, and output the multi-level energy utilization object set; Specifically, a multi-level energy utilization object set refers to dividing the original object set into multiple subsets based on the compatibility between the demand characteristics of the energy utilization objects and the multi-level thermal energy output characteristics. Each subset corresponds to a level of thermal energy output characteristics.

[0041] Specifically, the matching dimensions are clearly defined, and the parameters in the set of thermal energy demand information are aligned with the multi-level thermal energy output characteristics. The K-means clustering algorithm is used to group the energy utilization object set based on the Euclidean distance between the demand characteristics and the supply characteristics. At the same time, the analytic hierarchy process is combined to assign weights to different matching dimensions. The clustering results are verified using MATLAB's classification toolbox, and abnormal groups are removed. Each cluster subset is associated with the corresponding cascade thermal energy output characteristics to output the multi-level energy utilization object set.

[0042] This step, through quantitative demand-supply characteristic matching and clustering, transforms energy utilization objects into groups that precisely correspond to the characteristics of cascaded thermal energy output, thus avoiding the mismatch problem of underutilization of high-temperature energy and idleness of low-temperature energy.

[0043] Step S500: Establish the connection relationship between the multi-level energy utilization object set and the multiple fluid inlets and multiple independent heat exchange channels, and perform geothermal energy downhole heat exchange cascade optimization transmission based on the connection relationship.

[0044] Specifically, based on the demand characteristics of the multi-level energy utilization object set and the supply characteristics of the fluid inlet and flow channel, a connection relationship table of "object set - inlet - flow channel" is constructed using a MySQL database. According to the connection relationship and the allocated heat power, the basic operating parameters of each flow channel are determined. The parameters are then imported into the PLC control system to adjust the opening of the electric regulating valve at the fluid inlet, forming a cascade optimized transmission closed loop.

[0045] By establishing a clear connection between "object set - inlet - flow channel", the mixing and mismatch of different cascade heat energy are avoided, ensuring that high-grade geothermal resources prioritize meeting high-demand scenarios. The independent flow channel design reduces heat loss of media at different temperatures, and improves the economy and stability of downhole geothermal heat exchange as a whole, providing a feasible transmission solution for large-scale cascade utilization.

[0046] Furthermore, the step of extracting thermal output features based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data to obtain multi-level thermal energy output features includes: A time-series feature analysis is performed on the historical thermal energy monitoring data and the historical hot flow channel fluid monitoring data to extract the thermal energy output characteristics corresponding to each fluid inlet; the thermal energy output characteristics include at least the maximum output thermal energy, the average output thermal energy, the thermal energy stability index, and the thermal recovery characteristics. The heat output characteristics are normalized to obtain standardized multi-level heat output characteristics.

[0047] Specifically, maximum output thermal energy refers to the maximum amount of thermal energy that a fluid inlet can output during a historical monitoring period, reflecting the upper limit of the inlet's thermal energy supply. Average output thermal energy refers to the average thermal energy output of a fluid inlet during a historical monitoring period, reflecting its long-term stable supply capacity. The thermal energy stability index is used to quantify the degree of fluctuation in thermal energy output; the smaller the value, the more stable the output. Thermal recovery characteristics refer to the time and recovery efficiency required for a fluid inlet to stop outputting thermal energy and return to a stable state after continuous output.

[0048] Specifically, historical data from multiple fluid inlets are preprocessed to remove outliers caused by sensor malfunctions, such as sudden jumps in ultra-high temperature data at a certain inlet, and short-term missing data are supplemented by linear interpolation. The STL time series decomposition method in the Statsmodels library is used for time series feature analysis, splitting the data into trend, periodic, and residual terms to clarify the time-varying patterns of thermal energy output at each inlet, such as whether deep inlets are less affected by seasonality. Subsequently, based on the analysis results, numerical features such as maximum and average thermal energy output are calculated using the NumPy library, thermal stability indices are calculated using the Scikit-learn library, and thermal recovery curves are plotted using Matplotlib to determine thermal recovery characteristics. Finally, the Min-Max normalization formula is used, where the normalized value = (original feature value - minimum feature value) / (maximum feature value - minimum feature value)), mapping all features to the [0, 1] interval, ultimately outputting multi-level thermal energy output features corresponding to different tier depths.

[0049] By analyzing time-series characteristics, the temporal patterns of geothermal output at different cascade depths were accurately captured. Combined with normalization processing, differences in feature dimensions were eliminated, making the multi-stage thermal energy output characteristics directly comparable. This provides a standardized and quantifiable basis for subsequent matching of different energy utilization targets, avoiding matching deviations caused by incomparable features and significantly improving the accuracy of cascade utilization schemes.

[0050] Furthermore, the step of analyzing the heat energy usage demand information set based on the multi-level heat energy output characteristics, and dividing the energy utilization object set to output a multi-level energy utilization object set includes: Feature extraction is performed on the set of thermal energy demand information to obtain multi-level thermal energy demand characteristics; the multi-level thermal energy demand characteristics include at least the demanded thermal energy temperature, average heat load, thermal energy stability and thermal regulation flexibility. Based on the multi-level thermal energy output characteristics and the multi-level thermal energy demand characteristics, an optimization is performed using a preset objective function to output a multi-level energy utilization object set; the multi-level energy utilization object set is a mapping table of connection relationships between the multiple fluid inlets and the energy utilization objects; wherein, the objective function is used to maximize the comprehensive benefits of thermal energy output utilization rate and thermal energy consumption value weight.

[0051] Specifically, multi-level thermal energy demand characteristics refer to a set of quantitative indicators extracted from the thermal energy usage demand information set, reflecting the differences in demand among different energy utilization objects. These indicators correspond to different tiers of demand and include four core sub-characteristics: Demand Thermal Energy Temperature: the range of thermal energy temperatures required for the normal operation of the energy utilization object; Average Thermal Load: the average thermal energy consumption power of the object per unit time; Thermal Energy Stability: the object's tolerance to fluctuations in thermal energy supply; Thermal Regulation Elasticity: the adjustable range of the object's demand over time. The connection mapping table records the correspondence between "fluid inlet - energy utilization object," clearly specifying which inlet supplies thermal energy to which object and the allocation of the supplied thermal power.

[0052] Specifically, feature extraction is performed on the set of thermal energy demand information. Multi-level thermal energy demand features, such as demand temperature and average heat load, are extracted from raw demand data, such as temperature records and load fluctuation curves, and standardized into numerical vectors. An objective function is defined as "Objective function value = Σ (thermal energy output utilization rate of a fluid inlet × thermal energy consumption value weight of the corresponding object)", where the utilization rate is calculated using "actual supplied thermal power / maximum output thermal power of the inlet", and the weights are determined using the Analytic Hierarchy Process (AHP). An optimization model is built using the Pyomo modeling tool. Multi-level thermal energy output features and multi-level thermal energy demand features are input, and the Gurobi optimization solver is called for optimization. The goal is to maximize the objective function value and solve for the optimal "fluid inlet-object" correspondence. The optimization results are organized into a connection relationship mapping table, which includes information such as inlet number, object name, allocated thermal power, and matching degree score, thus outputting a set of multi-level energy utilization objects.

[0053] This step avoids the subjectivity of relying on experience-based classification by quantifying demand characteristics and optimizing the objective function. The objective function balances utilization rate and value weight, improving overall thermal energy utilization while prioritizing high-priority demands. This provides a clear supply-demand correspondence blueprint for subsequent heat exchange stage optimization and lays the core foundation for efficient system operation.

[0054] Furthermore, the optimization using a preset objective function includes: Obtain multiple optimization constraints; the multiple optimization constraints include at least temperature matching constraints, load matching constraints, stability matching constraints, and adjustment flexibility matching constraints; Under the premise of satisfying multiple optimization constraints, the objective function is optimized based on the multi-level thermal energy output characteristics and the multi-level thermal energy demand characteristics to obtain the optimal set of multi-level energy utilization objects.

[0055] Specifically, optimization constraints refer to the limitations set during the optimization process of the objective function to ensure the feasibility of the solution. These constraints filter out matching schemes that do not conform to the actual application scenario, avoiding theoretically optimal but practically infeasible results. Temperature matching constraints ensure that the temperature of the thermal energy output from the fluid inlet meets the required temperature range of the energy utilization object, ensuring that the thermal energy can be effectively utilized. Load matching constraints ensure that the thermal power allocated to the object by the fluid inlet is within a reasonable range, avoiding underload or overload. Stability matching constraints ensure that the fluctuation of the thermal energy output from the fluid inlet is below the tolerance threshold of the object, ensuring stable operation. Adjustment flexibility matching constraints ensure that the adjustment range of the thermal output from the fluid inlet covers the range of changes in the object's needs, meeting the dynamic requirements of the object.

[0056] Specifically, based on the multi-level thermal energy demand characteristics of the energy utilization object and the multi-level thermal energy output characteristics of the fluid inlet, the thresholds of each optimization constraint are quantified: for example, the upper and lower limits of temperature matching constraints are organized using Excel, and the boundary values ​​of load matching are calculated; the quantified constraints are integrated into the optimization model built by Pyomo, and together with the objective function, they constitute the mathematical expression of the optimization problem; then, the Gurobi optimization solver is called to optimize the objective function within the solution space defined by the constraints, and feasible solutions that satisfy all constraints are screened using the branch and bound method, and the scheme with the largest objective function value is selected from them; finally, the optimal connection relationship that meets the constraints is output, ensuring that the relationship satisfies both the actual operation requirements and achieves the optimal objective function.

[0057] By introducing optimization constraints, theoretically feasible but practically infeasible solutions are effectively avoided during the objective function optimization process, ensuring that the output "fluid inlet-object" connection relationship is practically feasible. Simultaneously, the combination of constraints and the objective function maximizes thermal energy utilization and value weight while ensuring stable operation of the object, making the cascade utilization scheme both scientific and practical.

[0058] Furthermore, the temperature matching constraint, load matching constraint, stability matching constraint, and adjustment elasticity matching constraint are used to constrain the values ​​corresponding to the thermal energy output characteristics of the fluid inlet to meet the requirements of the energy utilization object based on the thermal energy output characteristics.

[0059] Specifically, the actual characteristic value (thermal output characteristic) of the current fluid inlet is extracted from the multi-level thermal energy output characteristics, and the corresponding demand value of the object is extracted from the multi-level thermal energy demand characteristics. A lookup table of "characteristic item - inlet value - object demand value" is established in Excel. Based on the lookup table, the numerical rules of each constraint condition are defined: the temperature matching constraint is "inlet output temperature ≥ lower limit of object demand temperature and ≤ upper limit of demand temperature", the load matching constraint is "inlet distributed heat load ≥ average heat load of object", the stability matching constraint is "inlet thermal energy fluctuation coefficient ≤ maximum allowable fluctuation coefficient of object", and the adjustment elasticity matching constraint is "inlet adjustable range". "Object requirement adjustment range"; these rules are converted into Boolean expressions and integrated into the constraint module of the Pyomo optimization model; during the optimization process, the Gurobi solver verifies in real time whether each potential matching scheme satisfies all Boolean expressions, and only the schemes that pass the verification are retained to enter the objective function calculation stage.

[0060] By explicitly defining the mandatory matching requirements between "inlet characteristic value and object demand value," unreasonable schemes such as supplying high-temperature demand with low-temperature inlets or supplying high-load objects with low-load inlets are fundamentally avoided, ensuring that all candidate matching schemes meet the basic operational requirements of the objects. This not only improves the practical feasibility of the optimization results but also narrows the solution space for efficient optimization of the objective function, reduces invalid calculations, and makes the cascade optimization transmission scheme both theoretically optimal and stably adaptable to actual application scenarios.

[0061] Furthermore, the optimization of the objective function based on the multi-level heat energy output characteristics and the multi-level heat energy demand characteristics, under the premise of satisfying multiple optimization constraints, includes: Define output variables, which include the distribution relationship between the fluid inlet and the energy utilization object and the corresponding distributed thermal power; Under the premise of satisfying multiple optimization constraints, analyze the multi-stage heat energy output characteristics and the multi-stage heat energy demand characteristics to determine the solution space; An iterative optimization algorithm is used to search within the solution space, including: Select the first output variable that makes the objective function achieve its current optimal value; The solution space is iteratively updated based on the first combination of output variables to obtain the second output variable; The objective function value corresponding to the first output variable is compared with the objective function value corresponding to the second output variable until the preset convergence condition is met, and a multi-level energy utilization object set is output.

[0062] Specifically, the first output variable refers to the initial optimal solution selected from the solution space, whose corresponding objective function value is maximized in the current solution space. The second output variable is the new feasible solution obtained after adjusting the solution space based on the first output variable, used to compare the optimization effect with the first output variable.

[0063] Specifically, in the Pyomo optimization model, output variables are defined, with allocation relationships represented as 0-1 variables (1 indicating matching and 0 indicating mismatch), and allocated heat power represented as a continuous variable. Next, multi-level heat energy output characteristics and multi-level heat energy demand characteristics are input, and combined with optimization constraints, the constraint filtering function of the Gurobi solver is used to select all combinations of output variables that satisfy the constraints, constructing a solution space. A genetic algorithm is used to select the scheme with the largest objective function value from the solution space as the first output variable. Then, the solution space is iteratively updated based on the first output variable to obtain the second output variable. The difference between the two objective function values ​​is calculated; if the preset convergence condition is not met, the iteration is repeated until the difference is ≤ a threshold, and the final combination of output variables, i.e., the set of multi-level energy utilization objects, is output.

[0064] Through an iterative optimization mechanism, this step overcomes the limitation of selecting only the initial optimal solution, gradually approaching the globally optimal solution in the solution space and avoiding getting trapped in local optima. Simultaneously, preset convergence conditions ensure that the iterative process stops promptly when the optimization effect stabilizes, balancing optimization accuracy and computational efficiency. The final output set of multi-level energy utilization objects satisfies all constraints and maximizes thermal energy utilization and value weight, providing precise and efficient support for the cascade optimization and transmission of geothermal energy.

[0065] Furthermore, the optimized downhole heat exchange transmission of geothermal energy based on the connection relationship includes: Based on the allocation relationship and the allocated heat power, the valve opening degree of multiple valves is controlled by mapping the timing nodes and control opening parameters to obtain the valve opening degree sequence. The geothermal energy downhole heat exchange cascade optimization is performed according to the valve opening sequence. The multiple fluid inlets are controlled by multiple valves.

[0066] Specifically, valves are installed at each fluid inlet as flow control components. By changing the valve opening, they regulate the flow rate of the heat exchange medium into the independent heat exchange channel, thereby controlling the thermal energy output power. The valve opening sequence refers to a combination table of "control timing nodes - corresponding valve opening parameters" arranged in chronological order, which is the execution instruction for automatic valve control.

[0067] Specifically, a "fluid inlet - valve" correspondence table is established to clarify the control valve corresponding to each allocation relationship; based on the fluid dynamics model, a "distributed heat power - valve opening" mapping formula is established through FluidSIM software simulation, and the mapping curve is fitted to convert the optimized distributed heat power into control opening parameters; combined with the dynamic demand law of the energy utilization object, the control timing nodes are determined through MATLAB's time series analysis tool, and the "node-opening" combination is integrated into a valve opening sequence; finally, the opening sequence is converted into an electrical signal through a PLC control system to drive the valve positioner to perform adjustment, while the valve status is monitored in real time through a SCADA system to ensure precise control according to the sequence.

[0068] This step transforms the abstract optimization scheme into executable hardware control commands. Through precise adjustment of the valve opening sequence, it ensures that heat energy at each depth level is allocated according to demand. For example, high-temperature heat energy from deep layers is prioritized for power plants, while heat energy from mid-layers dynamically matches heating load fluctuations. Compared to traditional manual adjustment, automated valve control reduces heat energy transmission errors and improves cascade utilization efficiency. Simultaneously, the timing node settings adapt to the dynamic needs of the system, avoiding oversupply or undersupply, and achieving efficient and stable cascade transmission of geothermal energy in underground wells.

[0069] In summary, this invention, by employing a segmented coaxial sleeve with multiple inlets and multiple flow channels, combined with supply-demand matching logic, overcomes the limitations of traditional single-depth acquisition and blind allocation. It provides a complete solution for precisely supplying geothermal energy to different users at tiered depths, comprehensively improving the systematic nature and efficiency of geothermal energy utilization and laying the foundation for subsequent detailed optimization. Specifically, it first mines the temporal patterns of historical data through time-series feature analysis and eliminates differences in feature dimensions through normalization, thereby transforming the chaotic raw monitoring data into comparable multi-level thermal energy output characteristics. This avoids the one-sidedness of a single data point, accurately presents the geothermal supply capacity at each tiered depth, provides an accurate and standardized supply-side profile for subsequent supply-demand matching, and reduces the impact of feature analysis bias on the matching results. Furthermore, by extracting multi-level thermal energy demand characteristics to quantify object demand, and combining this with an objective function containing utilization rate and value weights for optimization, the traditional empirical object partitioning is replaced. This process takes into account both geothermal energy utilization efficiency and demand priority, avoids mismatch between high temperature and low utilization, and outputs a clear "fluid inlet-object" connection relationship mapping table, providing accurate demand-side matching basis for subsequent optimized transmission, and improving the scientificity and practicality of the partitioning results.

[0070] Example 2 Based on the same inventive concept as the comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization in the foregoing embodiments, such as Figure 2As shown, this embodiment provides a comprehensive optimization system for downhole heat exchange of geothermal energy for cascaded energy utilization. The system includes: Historical data acquisition module 11: used to acquire historical thermal energy monitoring data and historical hot flow channel fluid monitoring data of multiple fluid inlets; the fluid inlets are the fluid inlets of the segmented coaxial casing downhole of the geothermal well, the multiple fluid inlets correspond to multiple downhole stepped depths, and the segmented coaxial casing also includes multiple independent heat exchange channels; Output feature extraction module 12: used to extract thermal energy output features based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data, and obtain multi-level thermal energy output features; Demand information extraction module 13: used to obtain a set of thermal energy usage demand information for multiple energy utilization objects; The object output module 14 is used to analyze the set of thermal energy usage demand information based on the multi-level thermal energy output characteristics, divide the set of energy utilization objects, and output a multi-level set of energy utilization objects. Connection relationship establishment module 15: used to establish the connection relationship between the multi-level energy utilization object set and the multiple fluid inlets and multiple independent heat exchange channels, and to perform geothermal energy downhole heat exchange cascade optimization transmission based on the connection relationship.

[0071] Example 3 This embodiment provides a computer device including a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to realize a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a comprehensive optimization method for geothermal energy downhole heat exchange oriented towards energy cascade utilization.

[0072] Example 4 This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the comprehensive optimization method for geothermal energy downhole heat exchange oriented towards energy cascade utilization in the above embodiment.

[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A comprehensive optimization method for downhole heat exchange in geothermal energy wells for cascaded energy utilization, characterized in that, Includes the following steps: Historical thermal energy monitoring data and historical hot flow channel fluid monitoring data of multiple fluid inlets are acquired; the fluid inlets are the fluid inlets of the segmented coaxial casing of the geothermal well, the multiple fluid inlets correspond to multiple downhole stepped depths, and the segmented coaxial casing also includes multiple independent heat exchange channels; Based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data, thermal energy output characteristics are extracted to obtain multi-level thermal energy output characteristics. Acquire a set of thermal energy usage demand information from multiple energy utilization objects; Based on the multi-level thermal energy output characteristics, the thermal energy usage demand information set is analyzed, and the energy utilization object set is divided to output a multi-level energy utilization object set. Establish the connection relationship between the multi-level energy utilization object set and the multiple fluid inlets and multiple independent heat exchange channels, and optimize the downhole heat exchange transmission of geothermal energy based on the connection relationship.

2. The comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization, as described in claim 1, is characterized in that... The step of extracting thermal output features based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data to obtain multi-level thermal energy output features includes: A time-series feature analysis is performed on the historical thermal energy monitoring data and the historical hot flow channel fluid monitoring data to extract the thermal energy output characteristics corresponding to each fluid inlet; the thermal energy output characteristics include at least the maximum output thermal energy, the average output thermal energy, the thermal energy stability index, and the thermal recovery characteristics. The heat output characteristics are normalized to obtain standardized multi-level heat output characteristics.

3. The comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization, as described in claim 1, is characterized in that... The process involves analyzing the set of thermal energy usage demand information based on the multi-level thermal energy output characteristics, dividing the set of energy utilization objects, and outputting a multi-level energy utilization object set, including: Feature extraction is performed on the set of thermal energy demand information to obtain multi-level thermal energy demand characteristics; the multi-level thermal energy demand characteristics include at least the demanded thermal energy temperature, average heat load, thermal energy stability and thermal regulation flexibility. Based on the multi-level thermal energy output characteristics and the multi-level thermal energy demand characteristics, an optimization is performed using a preset objective function to output a multi-level energy utilization object set; the multi-level energy utilization object set is a mapping table of connection relationships between the multiple fluid inlets and the energy utilization objects; wherein, the objective function is used to maximize the comprehensive benefits of thermal energy output utilization rate and thermal energy consumption value weight.

4. The comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization, as described in claim 3, is characterized in that... The optimization using a preset objective function includes: Obtain multiple optimization constraints; the multiple optimization constraints include at least temperature matching constraints, load matching constraints, stability matching constraints, and adjustment flexibility matching constraints; Under the premise of satisfying multiple optimization constraints, the objective function is optimized based on the multi-level thermal energy output characteristics and the multi-level thermal energy demand characteristics to obtain the optimal set of multi-level energy utilization objects.

5. The comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization, as described in claim 4, is characterized in that... The temperature matching constraint, load matching constraint, stability matching constraint, and adjustment elasticity matching constraint are used to constrain the values ​​corresponding to the thermal energy output characteristics of the fluid inlet to meet the requirements of the energy utilization object based on the thermal energy output characteristics.

6. The comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization, as described in claim 4, is characterized in that... The optimization of the objective function based on the multi-level heat output characteristics and the multi-level heat demand characteristics, under the premise of satisfying multiple optimization constraints, includes: Define output variables, which include the distribution relationship between the fluid inlet and the energy utilization object and the corresponding distributed thermal power; Under the premise of satisfying multiple optimization constraints, analyze the multi-stage heat energy output characteristics and the multi-stage heat energy demand characteristics to determine the solution space; An iterative optimization algorithm is used to search within the solution space, including: Select the first output variable that makes the objective function achieve its current optimal value; The solution space is iteratively updated based on the first combination of output variables to obtain the second output variable; The objective function value corresponding to the first output variable is compared with the objective function value corresponding to the second output variable until the preset convergence condition is met, and a multi-level energy utilization object set is output.

7. A comprehensive optimization method for downhole heat exchange of geothermal energy oriented towards energy cascade utilization, as described in claim 6, is characterized in that... The optimized downhole heat exchange and transmission of geothermal energy based on the aforementioned connection relationship includes: Based on the allocation relationship and the allocated heat power, the valve opening degree of multiple valves is controlled by mapping the timing nodes and control opening parameters to obtain the valve opening degree sequence. The geothermal energy downhole heat exchange cascade optimization is performed according to the valve opening sequence. The multiple fluid inlets are controlled by multiple valves.

8. A comprehensive optimization system for downhole heat exchange of geothermal energy for cascaded energy utilization, characterized in that, include: Historical data acquisition module: used to acquire historical thermal energy monitoring data from multiple fluid inlets and historical hot runner fluid monitoring data; The fluid inlet is the fluid inlet of the segmented coaxial casing downhole of the geothermal well. The multiple fluid inlets correspond to multiple downhole stepped depths. The segmented coaxial casing also includes multiple independent heat exchange channels. Output feature extraction module: used to extract thermal energy output features based on the historical thermal energy monitoring data and historical hot flow channel fluid monitoring data, and obtain multi-level thermal energy output features; Demand information extraction module: used to obtain a set of thermal energy usage demand information from multiple energy utilization objects; The object output module is used to analyze the set of thermal energy usage demand information based on the multi-level thermal energy output characteristics, divide the set of energy utilization objects, and output a multi-level set of energy utilization objects. Connection relationship establishment module: used to establish the connection relationship between the multi-level energy utilization object set and the multiple fluid inlets and multiple independent heat exchange channels, and to perform geothermal energy downhole heat exchange cascade optimization transmission based on the connection relationship.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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