Dynamic evaluation method for water-heat type geothermal resources with balanced exploitation and afforestation

Abstract

The present application relates to geothermal resource development and evaluation technical field, especially to the balanced water and heat type geothermal resource dynamic evaluation method, including: collecting the flow, temperature and pressure data of the produced fluid and the recharging fluid, and carrying out time synchronization, outlier elimination and time series reconstruction processing on the data to form a continuous operation data set; jointly analyzing the time series change of the operation data set, extracting flow response characteristic quantity and heat exchange response characteristic quantity, and dividing the underground heat reservoir into flow and heat storage units according to the similarity of the characteristic quantity; on the scale of the flow and heat storage unit, respectively establishing a flow structure model and a heat exchange structure model, and coupling them, through a unified state variable to describe the corresponding relationship between the fluid migration behavior and the energy transfer behavior, a flow and heat storage dynamic evaluation model is constructed; based on the model, the change of the state variable of each flow and heat storage unit with time during the recharging operation process is calculated, and the dynamic evaluation data is obtained.

Description

Technical Field

[0001] This invention relates to the field of geothermal resource development and evaluation technology, and in particular to a dynamic evaluation method for geothermal resources with balanced extraction and irrigation hydrothermal characteristics. Background Technology

[0002] Hydrothermal geothermal resources are typically developed and utilized through a production and injection system consisting of production wells and injection wells. During the production and injection process, underground fluids migrate under the drive of pressure differences, accompanied by the carrying, exchange, and release of heat, forming an underground thermal process dominated by fluid circulation and convective heat transfer. Existing geothermal resource evaluation methods are mostly based on geological exploration data, reservoir parameter test results, or numerical simulation results to analyze the scale, temperature field distribution, and exploitable potential of underground reservoirs. Some methods also incorporate data such as flow rate, temperature, and pressure obtained during operation to assess the operating status of the geothermal system.

[0003] Existing technologies for geothermal resource assessment typically use fixed spatial zones or pre-set geological units as evaluation objects. This makes it difficult to dynamically divide underground thermal reservoirs based on changes in response characteristics such as flow rate, temperature, and pressure during extraction and irrigation operations. Consequently, it is difficult to form evaluation units that can reflect the actual response characteristics of underground thermal reservoirs under extraction and irrigation driving conditions. As a result, the evaluation results are difficult to accurately characterize the real flow and heat storage behavior of the underground system during extraction and irrigation operations. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a dynamic evaluation method for geothermal resources with balanced extraction and irrigation, aiming to improve the problem that the evaluation results are difficult to accurately characterize the actual heat flow and storage behavior of the underground system during extraction and irrigation operation.

[0005] This invention provides the following technical solution: a dynamic evaluation method for geothermal resources with balanced extraction and irrigation, comprising the following steps: S1. Collect flow rate data, temperature data, and corresponding pressure data of the extracted fluid and reinjected fluid, and perform time synchronization, outlier removal, and time series reconstruction to form a continuous operational dataset. S2. Perform joint analysis on the time-series changes of the running dataset, extract flow response characteristics and heat transfer response characteristics, and divide the areas with similar flow and heat transfer response characteristics in the underground thermal reservoir into flow and heat storage units based on the similarity of flow response characteristics and heat transfer response characteristics, using feature grouping or cluster analysis. S3. For each flow thermal storage unit, establish a flow structure model based on the response characteristics of the corresponding flow rate and pressure data; S4. Based on the flow structure model, establish a heat transfer structure model based on the response characteristics of the corresponding temperature data; S5. Couple the flow structure model and the heat transfer structure model at the flow heat storage unit level, and use unified state variables to describe the correspondence between fluid migration behavior and energy transfer behavior to construct a flow heat storage dynamic evaluation model. S6. Based on the dynamic evaluation model of heat storage and flow regulation, the changes of state variables corresponding to each heat storage and flow regulation unit over time during the irrigation and extraction process are calculated to obtain dynamic evaluation data.

[0006] By adopting the above technical solution, an evaluation method is realized that the underground thermal reservoir is dynamically divided and thermal storage units are constructed based on the flow, temperature and pressure response characteristics during the extraction and irrigation operation. This solves the problem that existing technologies use fixed spatial partitions or preset geological units as evaluation objects, which makes it difficult to accurately characterize the actual thermal storage behavior of the underground system during the extraction and irrigation operation.

[0007] Preferably, in S1, the acquisition of flow rate data, temperature data, and corresponding pressure data of the extracted fluid and reinjected fluid includes: At the wellhead, collect flow rate and temperature data of the produced fluid, and collect wellhead or downhole pressure data corresponding to the produced fluid. At the reinjection well, collect flow rate and temperature data of the reinjection fluid, and collect wellhead or downhole pressure data corresponding to the reinjection fluid; Data collected from production wells and reinjection wells are recorded according to a unified time base to form production fluid data sequences and reinjection fluid data sequences.

[0008] Preferably, in S1, the time synchronization, outlier removal, and timing reconstruction processes include: The flow rate, temperature, and pressure data of the extracted and reinjected fluids are time-stamped, and the data from different sources are time-aligned according to a unified time base. Based on the time-aligned data, data points that exceed the preset range of variation or do not meet the continuity requirements are identified, and the identified abnormal data points are removed from the data sequence. The data sequence after outlier removal is reconstructed over time to form a continuous data sequence of various types of data in the time dimension.

[0009] Preferably, in S2, the joint analysis of the time-series changes of the running dataset includes: Different types of data in the running dataset are aligned according to a unified time axis to form multivariate time series data; Analyze the synchronous change trends and magnitudes of multivariate time series data over the time dimension; Based on the synchronous change trend and change magnitude, the temporal correlation between different types of data is determined.

[0010] Preferably, in S2, the extraction of flow response characteristics and heat transfer response characteristics includes: Based on the temporal correlation between flow rate data and pressure data, flow response characteristics that characterize the migration properties of underground fluids are extracted; Based on the temporal variation characteristics of temperature data, heat transfer response features characterizing underground energy transfer are extracted. The flow response characteristics and heat transfer response characteristics are uniformly characterized to form a corresponding set of characteristics.

[0011] Preferably, in S2, dividing the areas with similar heat flow response characteristics in the underground thermal reservoir into heat flow storage units includes: Based on the flow response characteristics and heat transfer response characteristics, a flow and heat transfer response characteristic description for each region is constructed. Compare the consistency of the time-varying characteristics of the thermal response features in different regions; Regions exhibiting consistent temporal variation characteristics will be grouped into the same thermal storage unit.

[0012] Preferably, in S3, establishing the flow structure model includes: Based on the flow and pressure data of the corresponding flow thermal storage unit, a time-series response relationship reflecting the relationship between flow and pressure over time is constructed. Based on the time-series response relationship, identify the main migration direction and connectivity of the fluid within the thermal storage unit; The migration direction and connectivity are expressed in a structured form to form a flow structure model.

[0013] Preferably, in S4, establishing the heat transfer structure model includes: Based on the temperature data of the corresponding thermal storage unit, a time-series response relationship of temperature change over time is constructed; By combining flow structure models, the temporal distribution characteristics of temperature changes during fluid migration are analyzed; The temporal distribution characteristics of temperature changes are expressed in a structured form to form a heat transfer structure model.

[0014] Preferably, in S5, the description of the correspondence between fluid migration behavior and energy transfer behavior through a unified state variable includes: Based on the flow structure model, flow state variables characterizing the migration state of fluid within the heat storage unit are extracted. Based on the heat transfer structure model, heat transfer state variables characterizing the energy transfer state of fluid within the heat storage unit are extracted. By mapping the flow state variables and heat transfer state variables together, a unified state variable is formed to characterize the correspondence between the fluid migration state and the energy transfer state.

[0015] Preferably, in S6, the calculation of the changes in the state variables corresponding to each heat storage unit during the irrigation and drainage operation over time includes: According to the time sequence of the irrigation and drainage operation, the unified state variables corresponding to each heat storage unit are processed into time series. The changes of the unified state variable between adjacent time points are calculated to obtain the state change sequence of each heat storage unit. The state change sequences are summarized and processed to form corresponding dynamic evaluation data.

[0016] The present invention has the following beneficial effects: 1. In this invention, the underground thermal reservoir is divided into flow-heat storage units based on flow response characteristics and heat transfer response characteristics. This realizes a modeling method that uses flow-heat storage units as the basic objects of dynamic evaluation, which solves the problem that traditional evaluation objects, such as fixed spatial units or static geological zones, are difficult to reflect the underground response characteristics driven by extraction and irrigation.

[0017] 2. In this invention, by establishing and coupling a flow structure model and a heat transfer structure model at the scale of the heat storage unit, a structured joint characterization of fluid migration behavior and energy transfer behavior under the pumping and irrigation driving conditions is achieved, which solves the problem in existing evaluation methods that the flow process and heat transfer process are separated and difficult to characterize in a unified manner under the same evaluation framework.

[0018] 3. In this invention, by calculating the changes of a unified state variable with the operation time of the extraction and irrigation and forming dynamic evaluation data, the dynamic expression of the evolution characteristics of the geothermal system during the extraction and irrigation operation is realized, which solves the problem that it is difficult to reflect the dynamic change law under the condition of extraction and irrigation equilibrium when geothermal resource evaluation is based solely on static parameters or a single time section. Attached Figure Description

[0019] Figure 1 This is a flowchart of the dynamic evaluation method for balanced hydrothermal geothermal resources proposed in this invention. Detailed Implementation

[0020] The technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In the first embodiment of the present invention, the present invention provides a method for dynamic evaluation of geothermal resources with balanced extraction and irrigation, such as... Figure 1 As shown, it includes the following steps: S1. Collect flow rate data, temperature data, and corresponding pressure data of the extracted fluid and reinjected fluid, and perform time synchronization, outlier removal, and time series reconstruction to form a continuous operational dataset. Furthermore, in S1, the collection of flow rate data, temperature data, and corresponding pressure data of the extracted and reinjected fluids includes: At the wellhead, collect flow rate and temperature data of the produced fluid, and collect wellhead or downhole pressure data corresponding to the produced fluid. At the reinjection well, collect flow rate and temperature data of the reinjection fluid, and collect wellhead or downhole pressure data corresponding to the reinjection fluid; Data collected from production wells and reinjection wells are recorded according to a unified time base to form production fluid data sequences and reinjection fluid data sequences.

[0022] Furthermore, in S1, time synchronization, outlier removal, and timing reconstruction processes include: The flow rate, temperature, and pressure data of the extracted and reinjected fluids are time-stamped, and the data from different sources are time-aligned according to a unified time base. Based on the time-aligned data, data points that exceed the preset range of variation or do not meet the continuity requirements are identified, and the identified abnormal data points are removed from the data sequence. The data sequence after outlier removal is reconstructed over time to form a continuous data sequence of various types of data in the time dimension.

[0023] Specifically, the data acquisition and preprocessing in the dynamic evaluation method of balanced production and irrigation geothermal resources can be carried out under the normal production and irrigation operation of the geothermal system. Flow monitoring devices, temperature monitoring devices and pressure monitoring devices are respectively installed in the production well and the reinjection well. The collected data on the flow rate of the produced fluid, the temperature of the produced fluid, the pressure of the produced side, as well as the flow rate of the reinjection fluid, the temperature of the reinjection fluid, and the pressure of the reinjection side are transmitted to the data processing unit for unified processing through the data acquisition unit. During the data collection process, each piece of raw monitoring data is marked with a time stamp. The time stamp is generated based on a unified time reference to ensure comparability between different collection points and different data channels. The data collected from the production well side forms a produced fluid data sequence according to the unified time reference, and the data collected from the reinjection well side forms a reinjection fluid data sequence according to the unified time reference. The produced fluid data sequence and the reinjection fluid data sequence together constitute the raw operational data set. In time synchronization processing, data from different acquisition frequencies and at different acquisition times are time-series aligned, and a unified time axis can be represented as: ; in, Indicates the start time. Indicates a uniform sampling time interval. This is the time index number; For any type of monitoring data at the original time The observed value at point is denoted as The corresponding synchronization value on the unified timeline is denoted as ,when Falling and When the interval is between, linear interpolation can be used to obtain the result. ; in, and For the original data and Two adjacent sampling times; through the above processing, the flow, temperature, and pressure data from both the extraction and reinjection sides are mapped onto a unified time axis, forming multi-channel time-series data after time synchronization; in the outlier removal process, the time series of any data channel after time synchronization can be represented as: ; in, Indicate the length of the time series; first, calculate the median of the series. and absolute median difference: ; Set anomaly detection parameters When satisfied When an anomaly occurs, the corresponding data point is identified as an anomaly and removed from the sequence or marked as a missing value. This anomaly identification method can be used for anomaly identification in flow rate, temperature, and pressure data. The value can be set according to the operating conditions of irrigation and drainage, but there is no limit to the specific value; In time series reconstruction, the time series containing missing data after outlier removal is filled in to maintain the continuity of the data in the time dimension. For segments with a small number of missing points, linear interpolation of adjacent valid data points can be used for filling. For segments with a long number of missing points, smooth interpolation based on local time windows can be used. After filling, all types of data form a continuous data series on a unified time axis. After time synchronization, outlier removal, and time series reconstruction, the flow rate, temperature, and pressure data of the extracted and reinjected fluids form a continuous operational dataset within the same time frame. This operational dataset can fully reflect the temporal evolution of the fluid flow and heat transfer states during the extraction and reinjection process, providing a unified and continuous data foundation for subsequent joint analysis of the temporal changes in the operational dataset, extraction of flow response and heat transfer response characteristics, and division of flow-heat storage units.

[0024] S2. Perform joint analysis on the time-series changes of the running dataset, extract flow response characteristics and heat transfer response characteristics, and divide the areas with similar flow and heat transfer response characteristics in the underground thermal reservoir into flow and heat storage units based on the similarity of flow response characteristics and heat transfer response characteristics, using feature grouping or cluster analysis. Furthermore, in S2, the joint analysis of the time-series changes of the running dataset includes: Different types of data in the running dataset are aligned according to a unified time axis to form multivariate time series data; Analyze the synchronous change trends and magnitudes of multivariate time series data over the time dimension; Based on the synchronous change trend and change magnitude, the temporal correlation between different types of data is determined.

[0025] Furthermore, in S2, the extraction of flow response characteristics and heat transfer response characteristics includes: Based on the temporal correlation between flow rate data and pressure data, flow response characteristics that characterize the migration properties of underground fluids are extracted; Based on the temporal variation characteristics of temperature data, heat transfer response features characterizing underground energy transfer are extracted. The flow response characteristics and heat transfer response characteristics are uniformly characterized to form a corresponding set of characteristics.

[0026] Furthermore, in S2, regions within the underground thermal reservoir exhibiting similar heat flow response characteristics are divided into heat flow storage units, including: Based on the flow response characteristics and heat transfer response characteristics, a flow and heat transfer response characteristic description for each region is constructed. Compare the consistency of the time-varying characteristics of the thermal response features in different regions; Regions exhibiting consistent temporal variation characteristics will be grouped into the same thermal storage unit.

[0027] Specifically, after time synchronization, outlier removal and time series reconstruction are completed, the running dataset is input into the data analysis unit as a unified time series analysis object. The running dataset can be represented as a multivariate time series set composed of multiple physical quantities, where different physical quantities correspond to monitoring data such as flow rate, temperature and pressure. Each variable has a corresponding time index on a unified time axis, thus providing the basic conditions for subsequent joint analysis of time series changes. When performing joint analysis on the time-series changes of the running dataset, the different types of data in the running dataset are first aligned to a unified time axis. The time axis can inherit the unified time series formed in the previous stage. At every moment Below, data such as flow rate, temperature, and pressure are organized into a state vector form, denoted as: ; in, This represents the traffic data at the corresponding time. This represents the temperature data at the corresponding time. The pressure data at the corresponding moment is used to form a multivariate time series data set in the above way, so that different physical quantities can be expressed in relation to each other within the same time frame. After multivariate time series data are generated, their synchronous change trends and magnitudes over time are analyzed. Synchronous change trends characterize the overall consistency of different physical quantities changing over time, which can be achieved by comparing the direction of change of each variable's time series. Magnitudes characterize the relative degree of change of different physical quantities within the same time interval; for example, within a given time window... The change in the internally calculated variable can be expressed as: ; in, Represents any physical quantity at time t. The value below, This indicates the magnitude of change of the physical quantity within a time window. By comparing the signs and magnitudes of the changes in different physical quantities, their synchronous change characteristics can be determined. Based on the analysis of synchronous change trends and magnitudes, the temporal correlation between different types of data is further determined. This temporal correlation reflects the degree of correlation between flow rate, temperature, and pressure over time. For example, correlation analysis can be used to calculate the correlation between time series of different physical quantities. (The last sentence appears to be incomplete and possibly refers to a different topic.) and The temporal correlation degree can be expressed as: ; in, and These represent the average values ​​of the corresponding physical quantities over the time series. The length of the time series is represented by the above method, which is used to obtain the temporal correlation between different physical quantities and to characterize the joint change characteristics of multivariate time series data. After completing the time-series joint analysis, flow response characteristics and heat transfer response characteristics are further extracted based on the obtained time-series correlations. Flow response characteristics are derived from the time-series correlation between flow rate data and pressure data, and are used to reflect the dynamic characteristics of underground fluid migration behavior. For example, flow response characteristics can be expressed as the distribution characteristics of the correlation between flow rate and pressure over time or spatial location. Heat transfer response characteristics are derived from the time-series variation characteristics of temperature data, and are used to reflect the dynamic characteristics of underground heat transfer behavior. Heat transfer response characteristics can be expressed as the amplitude of temperature change, the rate of change, or their comprehensive characteristics within a time window. After the flow response and heat transfer response features are extracted, the two types of features are uniformly characterized. This uniform characterization can be achieved by constructing feature vectors. The feature vector corresponding to each spatial region can be represented as follows: ; in, This represents the flow response characteristic quantity corresponding to this region. This represents the heat transfer response characteristic quantity corresponding to the region. By using a unified characterization method, characteristic quantities with different physical meanings are integrated into the same characteristic space, which facilitates subsequent similarity analysis between regions. When dividing the thermal flow storage units, a thermal flow response feature description is constructed based on the feature vectors corresponding to each region. This description reflects the comprehensive dynamic characteristics of each region in terms of fluid migration and energy transfer. The consistency of the time-varying characteristics of the thermal flow response feature descriptions of different regions is measured by the similarity between feature vectors. For example, the distance or similarity between feature vectors can be used as a criterion. For any two regions... and The similarity of feature vectors can be expressed as: ; in, and Representing regions and region eigenvectors, The vector norm is used to determine the degree of consistency in temporal variation characteristics between different regions through the aforementioned similarity measures. Based on similarity comparison, regions can be classified using feature grouping or cluster analysis. Feature grouping can be based on similarity thresholds, while cluster analysis can use distance- or similarity-based clustering algorithms to automatically group feature vector sets. When multiple regions show consistency in the temporal variation characteristics of their flow-heat response features, they are classified into the same flow-heat storage unit, thus forming several storage units with similar flow-heat response behaviors in space. This storage unit serves as the basic analytical object for subsequent construction of flow structure models, heat transfer structure models, and dynamic evaluation analysis, enabling the temporal characteristics of operational data to be combined with the spatial structure of underground thermal reservoirs, thereby achieving an engineering expression of flow-heat storage behavior driven by extraction and irrigation.

[0028] S3. For each flow thermal storage unit, establish a flow structure model based on the response characteristics of the corresponding flow rate and pressure data; Furthermore, in S3, establishing the flow structure model includes: Based on the flow and pressure data of the corresponding flow thermal storage unit, a time-series response relationship reflecting the relationship between flow and pressure over time is constructed. Based on the time-series response relationship, identify the main migration direction and connectivity of the fluid within the thermal storage unit; The migration direction and connectivity are expressed in a structured form to form a flow structure model.

[0029] Specifically, for underground thermal storage areas where the flow-heat storage units have been divided, the flow structure model is established using the flow-heat storage units as the basic analysis objects. Each flow-heat storage unit corresponds to a set of flow and pressure data that have been synchronized and analyzed in time. These data are derived from the continuous monitoring results of the production well and the reinjection well during the production and injection process, and the time misalignment and abnormal interference have been eliminated in the previous steps, so as to truly reflect the dynamic response behavior of underground fluids under the production and injection driving conditions. When constructing the time-series response relationship between flow rate and pressure, flow rate and pressure data within the thermal storage unit are considered core variables describing the fluid migration state. Flow rate data reflects the exchange intensity of fluid between the wellbore and the thermal reservoir per unit time, while pressure data reflects the driving conditions and connectivity characteristics of the fluid within the thermal reservoir space, all within a unified time axis. Below, the flow time series can be denoted as: The pressure time series can be denoted as These two constitute a time-series response pair describing the dynamic behavior of fluids. To characterize the response relationship between the two over time, the correspondence between flow rate changes and pressure changes can be analyzed within a time window. For example, the time-series response relationship between flow rate and pressure can be expressed by calculating their response functions over time. These response functions describe the dynamic impact of pressure changes on flow rate changes. Under discrete-time conditions, the pressure change can be defined as: ; The change in flow rate at the corresponding time point is defined as: ; in, Indicates and The adjacent previous time point, through the and By analyzing the correspondence over time series, the response characteristics of flow rate to pressure changes can be obtained. These response characteristics can reflect the migration sensitivity and dynamic coupling characteristics of fluid under the drive of intake and irrigation. After obtaining the time-series response relationship between flow rate and pressure, the main migration direction and connectivity of the fluid in the thermal storage unit are further identified based on this time-series response relationship. The migration direction is used to characterize the dominant flow trend of the fluid from the recharge side to the production side or between different spatial locations during the recharge operation. The connectivity relationship is used to characterize whether there is an effective flow channel between different spatial regions. For example, the migration direction can be determined by analyzing the consistency between the pressure gradient and the flow rate change direction. When the pressure gradient direction and the flow rate increase direction are consistent in the time dimension, it can be considered that the fluid forms a stable migration path along this direction. In the process of connectivity identification, different spatial regions within the flow heat storage unit can be abstracted as nodes, and the time-series response relationship between flow rate and pressure can be used as the basis for connection between nodes. When two regions exhibit stable synchronous characteristics in terms of pressure change and flow rate response, it can be determined that there is a flow connectivity relationship between them. By comprehensively analyzing the connectivity relationship between different regions, a connectivity network reflecting the flow channel structure of fluid within the flow heat storage unit can be constructed. This connectivity network can reflect the actual migration path characteristics of fluid under the conditions of irrigation and drainage operation. After identifying the migration direction and connectivity, this information is expressed in a structured form to form a flow structure model. This structured representation can be achieved using a graph structure or a matrix structure; for example, a connectivity matrix can be constructed. , where matrix elements Representing a spatial region With spatial region The flow connectivity state between two entities; when there is a valid connectivity relationship between them. Take a non-zero value, otherwise take a zero value. At the same time, migration direction information can be added to the structured expression to distinguish the dominant direction of fluid migration. The resulting flow structure model fully describes the fluid migration path, dominant migration direction, and inter-regional connectivity under the internal extraction and irrigation driving conditions of the geothermal storage unit. As a structured abstract expression of underground fluid flow behavior, this model can be coupled with the subsequently established heat exchange structure model and provides a flow-level structural foundation for the dynamic evaluation model based on state variables. This allows the complex fluid migration behavior during extraction and irrigation operation to be expressed in an engineering-handleable model form, thereby supporting the overall operational logic of the dynamic evaluation method for hydrothermal geothermal resources under extraction-irrigation equilibrium conditions.

[0030] S4. Based on the flow structure model, establish a heat transfer structure model based on the response characteristics of the corresponding temperature data; Furthermore, in S4, establishing the heat transfer structure model includes: Based on the temperature data of the corresponding thermal storage unit, a time-series response relationship of temperature change over time is constructed; By combining flow structure models, the temporal distribution characteristics of temperature changes during fluid migration are analyzed; The temporal distribution characteristics of temperature changes are expressed in a structured form to form a heat transfer structure model.

[0031] Specifically, the establishment of the heat exchange structure model is carried out on the basis of the established flow structure model. The flow structure model gives the migration direction and connectivity between the spatial regions inside the flow heat storage unit. The temperature data comes from the temperature monitoring results of the extraction side, the reinjection side and representative locations within the flow heat storage unit during the extraction and recharge operation. The temperature data has been synchronized in time, removed outliers and reconstructed in the preprocessing stage, so that the time-series response behavior of heat generated by the forced circulation driven by extraction and recharge can be characterized on a unified time axis. When constructing the time-series response relationship of temperature change over time, the temperature time series of each spatial region within the thermal storage unit can be denoted as follows: ,in Indicates the first heat storage unit A spatial region or node Represents the first on the unified timeline At that moment, This represents the observed temperature value for that region at the corresponding time. For each spatial region, a temperature change series can be further constructed. ,in ; in Indicates and The adjacent previous moment, This is used to reflect the temperature response changes at adjacent time points, thereby forming a time-series response relationship that can characterize the temperature change over time. In one optional approach, it is also possible to... Smoothing is performed to reduce the impact of random disturbances on response recognition, but the smoothing method and window length are not limited. When analyzing the temporal distribution characteristics of temperature changes during fluid migration using a flow structure model, the migration direction and connectivity identified in the flow structure model can be considered as structural constraints for the propagation of the temperature response along the fluid migration path. This applies to any pair of regions with connectivity in the flow structure model. The arrival order and time offset of temperature changes during migration can be determined by calculating the time-delay correlation of the temperature response. The time-delay correlation function can be defined as: ; in Indicates the region Temperature change series and region The temperature change series in time lag The degree of correlation below Represents a non-negative integer time delay in units of sampling intervals. Indicates the length of the time series. express The mean, express The mean, by iterating through different The value of and determine The time delay for obtaining the extreme value can be used to obtain the region. Temperature response time lag This characterizes the temporal distribution of temperature changes along the migration path; In one alternative implementation, to further characterize the amplitude attenuation or propagation ratio of temperature changes during the migration process, a time delay can be obtained. Then, a linear mapping relationship was established between the temperature changes: ; in Indicates the region Temperature response transfer coefficient, This represents the residual terms not explained by the linear mapping. This can be obtained through least squares fitting, and in one possible way, the objective function can be set as: ; and take smallest As the fitting result, time delay With transmission coefficient The combination of these can simultaneously characterize the temporal and amplitude distribution of temperature changes during the migration process, thereby establishing a correspondence between heat transfer behavior and flow structure constraints. After completing the temporal distribution characteristic analysis of temperature changes, the temporal distribution characteristics of temperature changes are expressed in a structured form to form a heat transfer structure model. The structured expression can be consistent with the nodes and connectivity relationships of the flow structure model, which facilitates coupling at the same flow heat storage unit level. In one optional approach, a time delay matrix can be constructed. With the transfer coefficient matrix , where matrix elements Indicates the region Temperature response time lag Matrix elements Indicates the region Temperature response transfer coefficient When the region in the flow structure model With the region When there is no connectivity, the corresponding and It can be set to null or a preset flag, thereby constraining the temporal propagation relationship of the temperature response within a predetermined migration path; The heat transfer structure model formed through the above process uses the flow structure model as its framework and the time-series response relationship of temperature data as its basis. It expresses the time-series distribution characteristics of temperature changes in the fluid migration process in a structured way, so that the energy transfer behavior in the flow heat storage unit can be characterized in a calculable and associative structural form, and provides a structural basis for the heat transfer level for subsequent coupled description based on state variables.

[0032] S5. Couple the flow structure model and the heat transfer structure model at the flow heat storage unit level, and use unified state variables to describe the correspondence between fluid migration behavior and energy transfer behavior to construct a flow heat storage dynamic evaluation model. Furthermore, in S5, the correspondence between fluid migration behavior and energy transfer behavior is described through unified state variables, including: Based on the flow structure model, flow state variables characterizing the migration state of fluid within the heat storage unit are extracted. Based on the heat transfer structure model, heat transfer state variables characterizing the energy transfer state of fluid within the heat storage unit are extracted. By mapping the flow state variables and heat transfer state variables together, a unified state variable is formed to characterize the correspondence between the fluid migration state and the energy transfer state.

[0033] Specifically, the coupling treatment at the fluid heat storage unit level is carried out after both the flow structure model and the heat transfer structure model have been formed. The flow structure model provides the migration direction and connectivity between the spatial regions within the fluid heat storage unit, while the heat transfer structure model provides the temperature response time delay and temperature response transfer relationship under the constraints of the same spatial region and connectivity. The two have a consistent structural expression basis under the same node set and connection framework, thus enabling the construction of a unified state variable that can simultaneously characterize the fluid migration state and the energy transfer state at the same fluid heat storage unit scale. When extracting flow state variables characterizing the transition state, a structured representation of the flow structure model is used as input. The flow structure model can optionally be expressed as a connected matrix, where... Representing a spatial region With spatial region The connectivity state or connectivity strength between them, the flow state quantity is used to characterize the structural and dynamic characteristics of the fluid migration along the connectivity relationship in the flow heat storage unit. For example, the flow state quantity can be defined as the non-zero connection set and weight set of the connectivity matrix, and the driving response information related to the connection is introduced as a dynamic additional quantity. The dynamic additional quantity can be composed of the statistical characteristics of the flow rate change and the pressure change, thus forming a flow state quantity representation form that contains both structural information and response information. In one alternative implementation, to facilitate a unified mapping between flow state variables and heat transfer state variables, the flow state variables can be organized into a vector form, denoted as . The vector elements can correspond to connection pairs or node pairs in the flow structure model. If node pairs are used as indices, then the vector elements... It can be obtained by combining connectivity state and dynamic response information, and can optionally be represented as: ; in Represents node pairs The flow response weight, The value can be given by a normalized index composed of flow and pressure response characteristics. The normalization method can be selected by maximum-minimum normalization or mean-variance normalization, etc., but the specific form is not limited. The above organization method can make the flow state quantity expressed as a computable data structure under a unified index system. When extracting heat transfer state variables characterizing the energy transfer state, a structured representation of the heat transfer structure model is used as input. The heat transfer structure model can optionally be expressed as a time delay matrix and a transfer coefficient matrix, where... Represents node pairs Temperature response time lag, Represents node pairs The temperature response transfer coefficient, a heat transfer state variable, is used to characterize the temporal and amplitude characteristics of heat propagation along the migration channels within the heat storage unit. For example, the heat transfer state variable can be organized into a vector form, denoted as... Vector elements It can be obtained by combining time delay information and transmission information, and can optionally be represented as: ; in This represents a mapping function that combines time delay and transfer coefficient. The mapping function can optionally adopt a weighted combination method or a piecewise mapping method to reflect the differences in heat transfer characteristics of different connected channels. The specific form of the mapping function can be set according to engineering requirements, but it should be kept consistent within the same flow heat storage unit to ensure that the heat transfer state quantity can correspond to the flow state quantity under the same index system. When mapping the flow state variables and heat transfer state variables, a one-to-one correspondence is established between them in the node set and connection index, forming the basis for constructing a unified state variable. This mapping is used to establish the correspondence between migration states and energy transfer states, enabling the flow characteristics and heat transfer characteristics of the same migration channel to be expressed in parallel within the unified variable. For example, the unified state variable can be defined as a vector concatenation form, denoted as... And expressed as: ; in Represents the vector of flow state quantities. This represents the heat transfer state variable vector. The unified state variable vector is represented by vector concatenation, which allows the migration state and energy transfer state to be simultaneously characterized in the same variable. The unified state variable can be used as a time-series object in subsequent calculations to reflect the dynamic evolution during the irrigation and harvesting process. In another alternative implementation, the correlation mapping can also use a linear transformation to map the flow state variables to the heat transfer state variable space or vice versa, thereby forming unified state variables with coupling significance. The linear transformation can be expressed as: ; in and These represent the mapping matrices for the flow state variables and the heat transfer state variables, respectively. The dimensions of the mapping matrices match the indexing system. The mapping matrices can optionally use an identity matrix to maintain the contribution of the original state variables, or they can optionally use a weighted matrix based on connectivity to reflect the differences in importance of different channels. However, there is no limitation on the specific matrix construction method. Using the above methods can achieve the coupled expression of flow and heat transfer information while maintaining structural consistency. The unified state variables formed through the above coupling process use the migration channels of the flow structure model as the framework and the time-series propagation parameters of the heat transfer structure model as a supplement. This establishes a correspondence between fluid migration behavior and energy transfer behavior at the flow-heat storage unit level. Under the framework of unified state variables, a calculable and traceable dynamic evaluation model expression is formed. The sequence of the unified state variables evolving over time during the sampling and irrigation operation can be further used for the calculation and summarization of subsequent dynamic evaluation data. Thus, the dynamic characterization of the concept of "flow-heat storage" under the condition of sampling and irrigation equilibrium has a clear engineering implementation path and data processing logic.

[0034] S6. Based on the dynamic evaluation model of heat storage and flow regulation, the changes of state variables corresponding to each heat storage and flow regulation unit over time during the irrigation and extraction process are calculated to obtain dynamic evaluation data.

[0035] Furthermore, in S6, the calculation of the changes in state variables corresponding to each heat storage unit over time during the irrigation and drainage operation includes: According to the time sequence of the irrigation and drainage operation, the unified state variables corresponding to each heat storage unit are processed into time series. The changes of the unified state variable between adjacent time points are calculated to obtain the state change sequence of each heat storage unit. The state change sequences are summarized and processed to form corresponding dynamic evaluation data.

[0036] Specifically, the calculation of dynamic evaluation data is carried out after the unified state variables have been constructed and the coupled expression at the flow-heat storage unit level has been completed. The unified state variables are derived from the correlation mapping results of flow state variables and heat transfer state variables, and are continuously updated over time during the sampling and irrigation operation. Therefore, the calculation of the changes of state variables corresponding to each flow-heat storage unit over time during the sampling and irrigation operation can be implemented in the data processing unit in a time series manner. The dynamic evaluation data is output and stored as a summary expression of the time evolution process of state variables for subsequent analysis. When performing time-series processing on the unified state variables, a unified time index set is established for each flow-temperature thermal storage unit, based on the time sequence of sampling and irrigation operations. The time index can optionally be consistent with the unified time axis formed in the previous steps, or a new time index can be formed by resampling according to the actual sampling frequency and data integrity. At each moment, the unified state variable corresponding to the flow-temperature thermal storage unit is denoted as... ,in Indicates heat storage unit At any moment The unified state variable values, Number the heat storage unit. The index number is a time index, organized chronologically. It can form a heat storage unit. A unified state variable time series; When calculating the changes of a uniform state variable between adjacent time points, for any flow thermal storage unit At adjacent times and Define the state variable increment vector between them And can be expressed as: ; in This represents the change in the state of the unit between adjacent time points. Represents the unified state variable of the previous moment. The unified state variable representing the current moment is calculated by applying all time indices. A heat storage unit can be obtained. State change sequence This state change sequence can reflect the dynamic evolution trajectory of fluid migration and energy transfer at the coupled state variable level during the irrigation and drainage operation. In one alternative implementation, to facilitate comparison and unified summarization among different thermal storage units, the intensity of change in the incremental vector can be scalarized. This scalarization can be achieved using the vector norm, and the intensity of change can be defined as: ; in Represents the L2 norm, Indicates heat storage unit At any moment The intensity of state change can be stored in parallel with the original increment vector as a derived expression of the state change sequence. Scalar processing is not limited to using the second norm; the first norm, the infinite norm, or a weighted norm can also be used to reflect the weight differences of different state components. However, the weight setting is not limited to specific values. When summarizing and processing state change sequences to form dynamic evaluation data, time aggregation can be performed on the state change sequences according to the analysis cycle of irrigation and sampling operations. Time aggregation can be performed either by a sliding window or by a fixed interval, for example, by time interval. The dynamic evaluation data within can be obtained by accumulating the change intensity sequence and can be represented as: ; in Indicates heat storage unit In the time interval One of the dynamic evaluation data within, This indicates the time points within the specified time interval. The method for dividing the time interval can be selected based on the irrigation and sampling operation system, evaluation cycle, or data integrity, and its specific length is not limited. In addition to the cumulative method, statistical summarization methods such as mean, maximum value, or quantile can also be used. or The data are aggregated to form a dynamic evaluation dataset that can characterize the evolutionary features of the state. In another alternative implementation, the dynamic evaluation data can also retain vectorized results to reflect the cumulative changes of different state components over the time interval. The vectorized summary can be represented as: ; in Indicates heat storage unit In the time interval The cumulative state change vector within the data can be used to distinguish the contribution of flow state variables and heat transfer state variables in the coupled state variables in subsequent analysis by vectorizing the dynamic evaluation data. Vectorized summaries and scalarized summaries can be generated and stored in parallel to meet different dynamic evaluation expression needs. Through the above-mentioned time-series processing, calculation of changes at adjacent time points, and summary processing of state change sequences, a dynamic evaluation data output structure for each geothermal storage unit can be formed. The dynamic evaluation data can be continuously updated during the extraction and irrigation operation and stored in the data processing unit with time tags. This enables the geothermal storage dynamic evaluation model to have an executable calculation process and a traceable data expression form during engineering operation, thereby completing the formation process of dynamic evaluation data in the dynamic evaluation method of hydrothermal geothermal resources under the condition of balanced extraction and irrigation.

[0037] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic evaluation method for geothermal resources with balanced extraction and irrigation, characterized in that, Includes the following steps: S1. Collect flow rate data, temperature data, and corresponding pressure data of the extracted fluid and reinjected fluid, and perform time synchronization, outlier removal, and time series reconstruction to form a continuous operational dataset. S2. Perform joint analysis on the time-series changes of the running dataset, extract flow response characteristics and heat transfer response characteristics, and divide the areas with similar flow and heat transfer response characteristics in the underground thermal reservoir into flow and heat storage units based on the similarity of flow response characteristics and heat transfer response characteristics, using feature grouping or cluster analysis. S3. For each flow thermal storage unit, establish a flow structure model based on the response characteristics of the corresponding flow rate and pressure data; S4. Based on the flow structure model, establish a heat transfer structure model based on the response characteristics of the corresponding temperature data; S5. Couple the flow structure model and the heat transfer structure model at the flow heat storage unit level, and use unified state variables to describe the correspondence between fluid migration behavior and energy transfer behavior to construct a flow heat storage dynamic evaluation model. S6. Based on the dynamic evaluation model of heat storage and flow regulation, the changes of state variables corresponding to each heat storage and flow regulation unit over time during the irrigation and extraction process are calculated to obtain dynamic evaluation data.

2. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S1, the acquisition of flow rate data, temperature data, and corresponding pressure data of the extracted fluid and reinjected fluid includes: At the wellhead, collect flow rate and temperature data of the produced fluid, and collect wellhead or downhole pressure data corresponding to the produced fluid. At the reinjection well, collect flow rate and temperature data of the reinjection fluid, and collect wellhead or downhole pressure data corresponding to the reinjection fluid; Data collected from production wells and reinjection wells are recorded according to a unified time base to form production fluid data sequences and reinjection fluid data sequences.

3. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S1, the time synchronization, outlier removal, and timing reconstruction processes include: The flow rate, temperature, and pressure data of the extracted and reinjected fluids are time-stamped, and the data from different sources are time-aligned according to a unified time base. Based on the time-aligned data, data points that exceed the preset range of variation or do not meet the continuity requirements are identified, and the identified abnormal data points are removed from the data sequence. The data sequence after outlier removal is reconstructed over time to form a continuous data sequence of various types of data in the time dimension.

4. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S2, the joint analysis of the time-series changes of the running dataset includes: Different types of data in the running dataset are aligned according to a unified time axis to form multivariate time series data; Analyze the synchronous change trends and magnitudes of multivariate time series data over the time dimension; Based on the synchronous change trend and change magnitude, the temporal correlation between different types of data is determined.

5. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S2, the extraction of flow response characteristics and heat transfer response characteristics includes: Based on the temporal correlation between flow rate data and pressure data, flow response characteristics that characterize the migration properties of underground fluids are extracted; Based on the temporal variation characteristics of temperature data, heat transfer response features characterizing underground energy transfer are extracted. The flow response characteristics and heat transfer response characteristics are uniformly characterized to form a corresponding set of characteristics.

6. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S2, dividing areas with similar heat flow response characteristics in underground thermal reservoirs into heat flow storage units includes: Based on the flow response characteristics and heat transfer response characteristics, a flow and heat transfer response characteristic description for each region is constructed. Compare the consistency of the time-varying characteristics of the thermal response features in different regions; Regions exhibiting consistent temporal variation characteristics will be grouped into the same thermal storage unit.

7. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S3, establishing the flow structure model includes: Based on the flow and pressure data of the corresponding flow thermal storage unit, a time-series response relationship reflecting the relationship between flow and pressure over time is constructed. Based on the time-series response relationship, identify the main migration direction and connectivity of the fluid within the thermal storage unit; The migration direction and connectivity are expressed in a structured form to form a flow structure model.

8. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S4, establishing the heat transfer structure model includes: Based on the temperature data of the corresponding thermal storage unit, a time-series response relationship of temperature change over time is constructed; By combining flow structure models, the temporal distribution characteristics of temperature changes during fluid migration are analyzed; The temporal distribution characteristics of temperature changes are expressed in a structured form to form a heat transfer structure model.

9. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S5, describing the correspondence between fluid migration behavior and energy transfer behavior through unified state variables includes: Based on the flow structure model, flow state variables characterizing the migration state of fluid within the heat storage unit are extracted. Based on the heat transfer structure model, heat transfer state variables characterizing the energy transfer state of fluid within the heat storage unit are extracted. By mapping the flow state variables and heat transfer state variables together, a unified state variable is formed to characterize the correspondence between the fluid migration state and the energy transfer state.

10. The dynamic evaluation method for balanced hydrothermal geothermal resources according to claim 1, characterized in that, In S6, the calculation of the changes in the state variables of each heat storage unit over time during the irrigation and drainage operation includes: According to the time sequence of the irrigation and drainage operation, the unified state variables corresponding to each heat storage unit are processed into time series. The changes of the unified state variable between adjacent time points are calculated to obtain the state change sequence of each heat storage unit. The state change sequences are summarized and processed to form corresponding dynamic evaluation data.

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