A geothermal reservoir resource quantity evaluation system based on multi-source data

Abstract

The application discloses a geothermal heat reservoir resource quantity evaluation system based on multi-source data and concretely relates to the technical field of geothermal resource energy scheduling, and is used for solving the problem that the energy scheduling characteristics are disengaged from the multi-element subject demand due to the static resource total quantity evaluation of the evaluation system; the data acquisition module collects heat reservoir pressure dynamic monitoring data, tracer migration characteristic data and energy scheduling demand parameters; the demand identification module analyzes the power grid peak shaving response time efficiency demand and the heat supply system continuity demand; the feature extraction module generates pressure conduction efficiency features and heat breakthrough advantage path features; the collaborative analysis module establishes the matching rule of the pressure conduction efficiency and the response time efficiency, the coupling rule of the heat breakthrough path and the heat supply continuity, and forms the heat reservoir resource schedulability quantitative rule; the quota division module divides the schedulable quota according to the rule; the dynamic transfer module triggers the quota priority transfer when the heat supply demand is excessive; and the dynamic adaptation of the geothermal resource between the power peak shaving and the people's livelihood heat supply is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geothermal resource energy scheduling, and particularly relates to a geothermal heat reservoir resource quantity evaluation system based on multi-source data. BACKGROUND

[0002] In the process of constructing a geothermal energy supply system, heat reservoir resource quantity evaluation directly affects the stability and rationality of regional energy supply planning. The current evaluation system integrates geological structure, geothermal field distribution, fluid chemical characteristics and other multi-source data to form resource quantity evaluation conclusions, which serve as the basis for energy supply enterprises to deploy production capacity and for power grid peak shaving scheme development. Such evaluation results are widely used in the collaborative operation scenarios of multiple subjects such as energy supply security departments, power companies and regional heating institutions. In particular, in a multi-energy complementary energy supply system, a single heat reservoir needs to support different energy service needs such as base load power supply, peak shaving and standby, and domestic heating.

[0003] In the prior art, the dynamic adaptation capability of resource quantity evaluation conclusions to energy supply systems is insufficient. Since the evaluation system only provides static resource quantity indicators and does not associate the differentiated requirements of different energy supply subjects for resource scheduling characteristics (such as the load response rate required by power grid enterprises, the temperature stability required by heating enterprises, and the supply continuity required by livelihood departments), the energy supply scheme cannot accurately match the real-time scheduling needs of each subject. This defect causes the failure of key links in the energy supply chain, such as fluctuations in the heating system caused by sudden changes in power generation load or a sharp increase in social costs caused by interruptions in domestic heating. SUMMARY

[0004] The present application provides a geothermal heat reservoir resource quantity evaluation system based on multi-source data to solve the technical problems in the prior art.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] The present application provides the following technical solution:

[0007] A geothermal heat reservoir resource quantity evaluation system based on multi-source data, comprising:

[0008] A data acquisition module for acquiring heat reservoir pressure dynamic monitoring data, tracer migration characteristic data and scheduling demand parameters of energy supply subjects;

[0009] A demand identification module for identifying the response timeliness demand of the power grid peak shaving and standby system and the heating continuity demand of the domestic heating system based on the scheduling demand parameters;

[0010] A feature extraction module for extracting pressure conduction efficiency characteristics from the heat reservoir pressure dynamic monitoring data and simultaneously inverting heat breakthrough advantage path characteristics based on the tracer migration characteristic data;

[0011] a cooperative analysis module configured to cooperatively analyze a matching degree of the pressure conduction efficiency feature and the response timeliness requirement and a coupling relationship of the thermal breakthrough advantage path feature and the heat supply continuity requirement, and establish a thermal reservoir resource schedulability quantification rule;

[0012] a quota division module configured to divide a total resource quantity into a base load power schedulable quota and a civil heat supply schedulable quota by the thermal reservoir resource schedulability quantification rule;

[0013] a dynamic transfer module configured to start a priority transfer protocol of the base load power schedulable quota to the civil heat supply schedulable quota when the heat supply continuity requirement exceeds the civil heat supply schedulable quota.

[0014] Further, the thermal reservoir pressure dynamic monitoring data, the tracer migration characteristic data and the dispatching requirement parameters of the energy supply subject are acquired, including:

[0015] the thermal reservoir pressure dynamic monitoring data is collected by a pressure sensor arranged at a geothermal well head;

[0016] a fluorescent tracer is injected into the geothermal well according to a standard tracer test procedure, and the tracer migration characteristic data is recorded by a monitoring device;

[0017] the dispatching requirement parameters include a peak shaving standby load response requirement record acquired from a power grid dispatching system;

[0018] the dispatching requirement parameters include a civil heat supply continuity requirement acquired from a heat supply operation system.

[0019] Further, the response timeliness requirement of the power grid peak shaving standby system and the heat supply continuity requirement of the civil heat supply system are identified based on the dispatching requirement parameters, including:

[0020] a maximum allowable response delay time in the peak shaving standby load response requirement record is analyzed as the response timeliness requirement of the power grid peak shaving standby system;

[0021] a minimum temperature maintenance time threshold in the civil heat supply continuity requirement is extracted as the heat supply continuity requirement of the civil heat supply system.

[0022] Further, the pressure conduction efficiency feature is extracted according to the thermal reservoir pressure dynamic monitoring data, and the thermal breakthrough advantage path feature is inverted based on the tracer migration characteristic data, including:

[0023] a dynamic evolution relationship of a pressure change rate with a spatial distribution in the thermal reservoir pressure dynamic monitoring data is calculated to generate the pressure conduction efficiency feature representing a reservoir conduction capacity;

[0024] a spatial migration probability distribution of a tracer concentration peak value in the tracer migration characteristic data is analyzed to generate the thermal breakthrough advantage path feature representing a fluid advantage migration path.

[0025] Further, the pressure conduction efficiency feature is generated, including:

[0026] Based on the dynamic monitoring data of the heat reservoir pressure, the pressure change rate of each spatial position is calculated;

[0027] The spatial distribution evolution law of the pressure change rate in the reservoir three-dimensional space is analyzed;

[0028] According to the spatial distribution evolution law of the pressure change rate, the pressure conduction efficiency feature is generated, which directly reflects the spatial heterogeneity of the reservoir conduction capacity.

[0029] Further, the hot breakthrough advantage path feature is generated, including:

[0030] The tracer concentration peak value data is extracted from the tracer migration characteristic data;

[0031] The migration trajectory of the tracer concentration peak value in the reservoir three-dimensional space is tracked;

[0032] Based on the migration trajectory, the spatial migration probability distribution is calculated;

[0033] According to the spatial migration probability distribution, the hot breakthrough advantage path feature is generated, which directly represents the spatial distribution feature of the fluid advantage migration path.

[0034] Further, the matching degree of the pressure conduction efficiency feature and the response timeliness requirement, and the coupling relationship of the hot breakthrough advantage path feature and the heat supply continuity requirement are analyzed in coordination, and the heat reservoir resource schedulability quantization rule is established, including:

[0035] The pressure conduction efficiency feature and the response timeliness requirement are dynamically matched and compared, whether the pressure conduction efficiency feature meets the response timeliness requirement is judged, and the matching and comparison result is obtained;

[0036] The hot breakthrough advantage path feature and the heat supply continuity requirement are risk coupled and analyzed, the influence degree of the hot breakthrough advantage path feature on the heat supply continuity requirement is evaluated, and the coupling analysis result is obtained;

[0037] According to the combination relationship of the matching and comparison result and the coupling analysis result, the heat reservoir resource schedulability quantization rule is established.

[0038] Further, the acquisition logic of the coupling analysis result is:

[0039] The spatial distribution feature of the fluid advantage migration path in the hot breakthrough advantage path feature is analyzed;

[0040] The spatial position relationship between the fluid advantage migration path and the heat supply well pattern is compared;

[0041] Predicting the risk of hot fluid breakthrough based on spatial position relationship;

[0042] In combination with the minimum temperature maintenance duration threshold in the heat supply continuity requirement, the influence degree of the hot fluid breakthrough risk on the heat supply continuity is quantified;

[0043] A coupling analysis result characterizing the risk level is generated.

[0044] Further, the total resource quantity is divided into a base load power dispatchable quota and a civil heat supply dispatchable quota by a heat storage resource dispatchability quantification rule, including:

[0045] The quota allocation operation is performed on the total resource quantity by applying the heat storage resource dispatchability quantification rule, and the allocation operation is based on the combination relationship of the matching comparison result and the coupling analysis result;

[0046] The base load power dispatchable quota meeting the response time requirement and the civil heat supply dispatchable quota meeting the heat supply continuity requirement are generated.

[0047] Further, when the heat supply continuity requirement exceeds the civil heat supply dispatchable quota, a priority transfer protocol of the base load power dispatchable quota to the civil heat supply dispatchable quota is started, including:

[0048] The comparison relationship between the heat supply continuity requirement and the civil heat supply dispatchable quota is monitored in real time;

[0049] When the heat supply continuity requirement continuously exceeds the civil heat supply dispatchable quota, the priority transfer protocol is triggered;

[0050] The real-time transfer operation of the base load power dispatchable quota to the civil heat supply dispatchable quota is performed, and the transfer quantity is equal to the excess quantity of the heat supply continuity requirement exceeding the civil heat supply dispatchable quota.

[0051] The beneficial effects of the present application are:

[0052] 1、The system significantly improves the dynamic adaptation capability of geothermal resources in a multi-energy scenario; based on the matching analysis of the pressure conduction efficiency characteristics and the power grid response time requirement, the response capability of the heat storage resource to the power peak load is accurately quantified, so that the generation side can quickly call the standby resource when the load mutates; at the same time, through the coupling analysis of the heat breakthrough advantage path characteristics and the heat supply continuity requirement, the influence path of fluid migration on temperature stability is predicted, and the heat supply pipe network fluctuation risk is avoided from the source, so that the resource allocation scheme can meet the millisecond-level response requirement of the power system and the long-period stability requirement of the heat supply system at the same time, and the core contradiction that the resource scheduling characteristics are disconnected from the energy main body demand in the multi-energy complementary scenario is solved.

[0053] 2, The resource quota dynamic transfer chain is established, the priority guarantee of people's livelihood heating is realized, the rigid boundary of the base load power and the civil heating quota is generated according to the dispatchability rule, and when the heating demand is excessive, the quota rebalancing mechanism is automatically triggered through real-time monitoring and atomized transfer agreement, so that the continuity of people's livelihood heating is not affected by power load fluctuation under extreme working conditions, and the basic peak shaving capacity of the power grid is protected through the transfer amount constraint, the energy supply system resilience is improved under the premise of constant total resource, the geological resource characteristics are converted into executable dispatching strategy, and support is provided for regional energy security. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 It is a structure schematic view of a geothermal reservoir resource quantity evaluation system based on multi-source data of the present application.

[0055] Figure 2 It is a flow chart of establishing the quantification rule of reservoir resource dispatchability of the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0057] Embodiment: Figure 1 A structure schematic view of a geothermal reservoir resource quantity evaluation system based on multi-source data of the present application is given, a geothermal reservoir resource quantity evaluation system based on multi-source data, comprising:

[0058] A data acquisition module is used for acquiring reservoir pressure dynamic monitoring data, tracer migration characteristic data and dispatching demand parameters of energy supply subjects.

[0059] A demand identification module is used for identifying the response time demand of the power grid peak shaving standby system and the heating continuity demand of the civil heating system based on the dispatching demand parameters.

[0060] A feature extraction module is used for extracting pressure conduction efficiency features according to the reservoir pressure dynamic monitoring data, and inversely calculating the hot breakthrough advantage path features based on the tracer migration characteristic data.

[0061] A collaborative analysis module is used for collaboratively analyzing the matching degree of the pressure conduction efficiency features and the response time demand, and the coupling relationship of the hot breakthrough advantage path features and the heating continuity demand, and establishing the quantification rule of reservoir resource dispatchability.

[0062] a quota division module, configured to divide the total resource quantity into a base load power dispatchable quota and a civil heat supply dispatchable quota according to a heat storage resource dispatchability quantification rule;

[0063] a dynamic transfer module, configured to start a priority transfer protocol from the base load power dispatchable quota to the civil heat supply dispatchable quota when the heat supply continuity demand exceeds the civil heat supply dispatchable quota.

[0064] acquiring heat storage pressure dynamic monitoring data, tracer migration characteristic data and dispatching demand parameters of an energy supply subject, specifically implemented as:

[0065] acquiring heat storage pressure dynamic monitoring data through a pressure sensor arranged at a geothermal wellhead, specifically implemented by installing a high-precision pressure sensor in the annulus of the casing of the production interval of the geothermal well. The pressure sensor records the bottom hole pressure value at a fixed time interval (for example, every 5 minutes), and continuously acquires pressure time series data for not less than 30 days. The recorded pressure data includes absolute pressure value and temperature compensation value, and the pressure measurement range covers 0 to 25 megapascals, with an accuracy of 0.1% of the full range. The raw data acquired by the pressure sensor is converted into a standard engineering unit data packet through a wellhead data acquisition unit, which includes three core parameters of time stamp, pressure value and temperature value. During the data conversion process, zero drift correction and temperature compensation calculation are performed, and the corrected data packet is transmitted to the central processing unit for storage after integrity verification.

[0066] According to the standard tracer test procedure, a fluorescent tracer is injected into the geothermal well, and the tracer migration characteristic data is recorded by the monitoring equipment. Specifically, a fluorescent compound with a specific molecular weight range (for example, carboxyfluorescein sodium salt with a molecular weight less than 200 daltons) is selected as the tracer. The tracer injection operation includes the following steps: first, close the geothermal well production valve, and inject a specific concentration of tracer solution (for example, 100 milligrams per liter) into the wellbore annulus at a constant flow rate (for example, 10 liters per minute) through an injection pump. The total injection amount is calculated according to a specific proportion of the pore volume of the geothermal reservoir (for example, 0.05% of the pore volume). After the injection is completed, the production is restored, and an online fluorescence spectrometer is installed at the outlet wellhead to detect the tracer concentration at a fixed time interval (for example, every 15 minutes), and continuously monitor until the concentration decreases to the background value before injection. The recorded tracer migration characteristic data includes three parameters of time, concentration and flow rate, and the concentration detection sensitivity reaches a specific level (for example, 0.1 microgram per liter).

[0067] The dispatch demand parameter includes a peak shaving standby load response demand record obtained from the power grid dispatching system. In specific implementation, the real-time database of the power grid dispatching center is accessed through the power data interface protocol. The peak shaving standby load response demand record contains two core indicators: the load fluctuation threshold and the response time requirement. The load fluctuation threshold refers to the critical value of the minute-level load change of the power grid exceeding a certain proportion of the total installed capacity of the region (for example, exceeding 3%). The response time requirement refers to the maximum allowable delay time from the dispatch instruction to the target power output of the geothermal power station (for example, not more than 120 seconds). The above parameters are stored in the form of a structured data table, and the data table fields include demand number, effective period, threshold value, time constraint, and other necessary information items.

[0068] The dispatch demand parameter includes the civil heating continuity requirement obtained from the heating operation system. In specific implementation, the technical specification document of the heating operation system is parsed. The civil heating continuity requirement clearly specifies two key indicators: the minimum temperature requirement at the end of the heating network (for example, not less than 55 degrees Celsius), which needs to be continuously maintained during the heating season; and the temperature maintenance duration threshold, which refers to the maximum allowable duration of a single heating interruption event (for example, not more than 2 hours). The above parameters are stored in the heating system configuration file in a specific format (for example, in the form of key-value pairs), and the configuration item identifiers are temperature threshold identifier and interruption duration identifier, respectively.

[0069] The energy supply subject includes the power grid dispatching system and the heating operation system. The power grid dispatching system refers to the load dispatching platform operated by the power control center, and the heating operation system refers to the heat network monitoring center managed by the heating enterprise. Both of them are connected with the geothermal resource evaluation system through a standardized data interface, and the communication process adopts an identity authentication and data encryption mechanism. The authentication key is uniformly distributed by the energy management department, and the encryption process adopts a standard encryption algorithm. During data exchange, integrity check is performed, and when the check fails, a data retransmission mechanism is triggered.

[0070] Based on the dispatch demand parameter, the response time requirement of the power grid peak shaving standby system and the heating continuity requirement of the civil heating system are identified, and the specific implementation is as follows:

[0071] The response time requirement of the peak regulation system is identified based on the scheduling demand parameter, and a data analysis operation is performed on the peak regulation load response demand record in specific implementation. The peak regulation load response demand record is stored in a structured table form, and the table includes four fields of event number, timestamp, load change amount, and response time constraint. The response time constraint field is located first, which stores numerical data representing the maximum allowed response delay time (for example, an integer value of 120). After extracting the numerical value, unit verification is performed: if the original data does not have a unit, the second unit is automatically supplemented; if it has a non-second unit (such as minutes), unit conversion is performed (for example, 2 minutes is converted to 120 seconds). Finally, a parameter named response time requirement is generated, whose value is equal to the numerical value of the maximum allowed response delay time in seconds, which is written into the specified storage area of the database, and the field name of the storage area is exactly the same as the parameter name.

[0072] The minimum temperature maintenance duration threshold in the civil heating continuity requirement is extracted as the heating continuity requirement, and the text content of the civil heating continuity requirement document is analyzed in specific implementation. The document contains technical clauses described in natural language, and the target clause is located by keyword matching: first, scan the sentences containing keywords such as "temperature", "maintain", "threshold", and locate the specific clause (for example, "the minimum temperature maintenance duration of the heating terminal shall not be less than 2 hours"). Semantic analysis is performed on the located clause: the numerical part (for example, the number 2) and the unit part (for example, the Chinese character "hour") are extracted, and when the unit part is an illegal measurement unit (such as "clock"), it is converted to the standard unit (1 clock = 900 seconds). Finally, a parameter named minimum temperature maintenance duration threshold is generated, whose value is equal to the numerical value of the analysis result in seconds (for example, 2 hours is converted to 7200 seconds). After the parameter is generated, range verification is performed: if the value is less than 0 or greater than the pre-defined upper limit (for example, 86400 seconds), an exception handling process is triggered.

[0073] The exception handling process includes the following steps: recording an exception event log, the log entry includes the original parameter value, the exception type, and the occurrence time; calling the default parameter table to obtain the industry experience value (for example, the default value of the response time requirement is 180 seconds, and the default value of the minimum temperature maintenance duration threshold is 14400 seconds); writing the default value into the database and marking the exception state identifier; sending an alarm notification to the administrator console. The update mechanism of the default parameter table is: automatically downloading the latest industry recommended value from the energy management knowledge base on the first day of each quarter, and performing validity verification after downloading (for example, verifying whether the value is within the interval [60, 300] seconds).

[0074] The demand parameters are standardized in data format when passing to the subsequent processing flow. The response time requirement is converted to float format, with one decimal place for storage precision (e.g. 120.0 seconds). The minimum temperature duration threshold is converted to integer format, with the unit forced to seconds (e.g. 7200 seconds). The conversion process performs data backup, with the backup file containing the original value before conversion, the standardized value after conversion, and the conversion timestamp. When the parameters need to be transmitted across systems, they are packaged in JSON format, with the field names strictly following the full name naming specification (e.g. {"response_time_requirement": 120.0, "min_temperature_duration_threshold": 7200}).

[0075] The parameter storage architecture adopts a dual-replica mechanism. The primary replica is stored in the real-time database, with an update frequency synchronized with the data source; the secondary replica is stored in the historical archive, with incremental backup performed every morning. The access interface provides parameter query services, and the query request must include an application identifier for authentication. When querying the response time requirement, the current valid value and data source marker are returned; when querying the minimum temperature duration threshold, the heating season status identifier is additionally returned (e.g. the actual value is returned in the heating season activation state, and 0 is returned in the non-heating season). The heating season status switching rule is: the heating parameters are automatically activated on a fixed date each year (e.g. November 15th at 0 o'clock), deactivated on a fixed date the following year (e.g. March 15th at 24 o'clock), and the switching action triggers parameter validity review (e.g. checking whether the temperature threshold is between 40 and 70 degrees Celsius).

[0076] According to the dynamic monitoring data of the thermal reservoir pressure, the pressure conduction efficiency characteristics are extracted, and based on the tracer migration characteristic data, the hot breakthrough advantage path characteristics are inverted, which are specifically implemented as:

[0077] Based on the dynamic monitoring data of the thermal reservoir pressure, the pressure change rate of each spatial position is calculated. In the specific implementation, a three-dimensional spatial grid model of the reservoir is first established. The three-dimensional spatial grid model is composed of regularly arranged cubic units, and the size of each cubic unit is determined according to the geological exploration accuracy (e.g. the plane size is 50 meters x 50 meters, and the vertical thickness is 10 meters). For each grid unit, the pressure time series data of all monitoring points in the unit are obtained, the pressure data is in units of pascals, and the collection time interval is fixed (e.g. recorded every 5 minutes). When calculating the pressure change rate, a time window sliding calculation method is used: the time window length is set to a certain value (e.g. 60 minutes), and linear trend analysis is performed on the continuous pressure values in the window. The slope of the linear trend line is the pressure change rate at the center time of the time window, with the unit being pascals per hour. After calculation, a pressure change rate three-dimensional matrix is formed, with the row and column indices corresponding to the spatial grid coordinates, and the time dimension index corresponding to the time sequence point.

[0078] The spatial distribution evolution law of the pressure change rate is analyzed, and spatial interpolation is performed on the three-dimensional matrix of the pressure change rate in specific implementation. The spatial interpolation is based on the principle of spatial correlation. First, the spatial distance matrix between each grid cell is calculated, and the Euclidean formula is used for distance calculation. Then, a correlation model between distance and pressure change rate is established, and the correlation model is determined by experimental data fitting (for example, the correlation attenuation coefficient is 0.7 when the distance increases by 100 meters). Finally, the interpolation calculation is performed on the grid cells without monitoring data, and the interpolation weight is equal to the square of the reciprocal of the distance. The spatial distribution evolution law is displayed through the time series spatial distribution graph: select the key time node (for example, 24 hours after the start of water injection), and generate the pressure change rate distribution graph of the whole reservoir at that time. The distribution graph uses the color scale representation method, and the color scale level is set to a specific number (for example, divided into 10 levels). Different color regions correspond to different pressure change rate ranges, and the boundary value difference between adjacent color scales is a fixed value (for example, 5 kPa per hour).

[0079] The pressure conduction efficiency feature is generated according to the spatial distribution evolution law of the pressure change rate, and a conduction efficiency quantitative index is defined in specific implementation. The index is calculated as the spatial variation rate of the pressure change rate: in the three-dimensional grid model, for the target grid cell, the pressure change rate difference between the cell and the adjacent cells is calculated, and the absolute value is divided by the cell spacing to obtain the spatial variation rate value, which is in units of pascal per meter per hour. The conduction efficiency feature is represented by a three-dimensional data body composed of spatial variation rate values, and each element in the data body corresponds to the conduction efficiency value of the grid cell. The feature directly reflects the spatial difference of the reservoir conduction capacity: when the spatial variation rate value of a certain region is lower than a certain critical value (for example, 100 Pa per meter per hour), it is determined as a high conduction area; if it is higher than the critical value, it is determined as a low conduction area. After the feature data body is generated, standardization processing is performed to linearly convert the original value to the 0-100 interval, and the conversion formula is: standardized value = (original value-minimum value) / (maximum value-minimum value) x 100.

[0080] The tracer concentration peak value data is extracted from the tracer migration characteristic data, and in specific implementation, the extreme points of the concentration time series are first identified. For the concentration change curve of each monitoring well, the local maximum value detection method is used: set the detection window length to a certain number of points (for example, 5 consecutive recording points), and compare the concentration values of each point in the window; when the center point concentration is greater than the concentration values of the previous two points and the next two points, and exceeds the background concentration by a certain multiple (for example, 3 times the standard deviation), it is marked as a concentration peak point. The extracted peak value data includes three basic attributes: peak concentration value (in units of mg / L), peak occurrence time (accurate to seconds), and monitoring well three-dimensional coordinates (east coordinate, north coordinate, and elevation depth). For multiple peak points that occur in the same monitoring well, all peak point data is recorded in chronological order.

[0081] The migration trajectory of the tracer concentration peak in the three-dimensional space of the reservoir is tracked, and the spatial topological relationship of the peak points is established in implementation. The topological relationship is based on the geological structure of the reservoir, and the reservoir is divided into connected units, and the boundaries of the units are determined according to the lithological change interface. The tracking operation starts from the injection well position and connects the peak points in chronological order: when the peaks of adjacent time points appear in the same connected unit, a connection line segment is directly generated; when they appear in adjacent units, a polyline is generated along the shortest connected path between the units. The migration trajectory is represented by a sequence of spatial points, each point containing three-dimensional coordinates and a time stamp attribute. The length of the trajectory is calculated using the polyline accumulation formula, and the distance unit is meters, with a calculation precision of two decimal places.

[0082] The spatial migration probability distribution is calculated based on the migration trajectory, and a spatial frequency statistical algorithm is used in implementation. First, the reservoir space is divided into regular cubic grids (for example, 10m x 10m x 2m), and each grid is assigned a unique location code. For each migration trajectory, the sequence of grid codes it passes through is counted, and the total number of times each grid is crossed by the trajectory is accumulated. The spatial migration probability value is calculated as follows: the grid probability value is equal to the number of times the grid is crossed divided by the total number of trajectories multiplied by 100, and the result is expressed in percentage (for example, a certain grid is crossed by 30 out of 50 trajectories, so the probability value is 60%). Finally, a three-dimensional probability distribution matrix is formed, and each element in the matrix represents the probability value of the tracer flowing through the corresponding grid.

[0083] The hot breakthrough advantage path feature is generated according to the spatial migration probability distribution, and the path feature extraction rules are defined in implementation. The identification criteria for the hot breakthrough advantage path feature are as follows: select grid units with a probability value greater than a set threshold (for example, 60%); mark the spatially continuous unit set as an independent channel. The feature parameters of each channel include three dimensions: channel length (distance between two end points along the migration direction), average width (maximum cross-sectional span perpendicular to the migration direction), and advantage direction (angle between channel axis and horizontal plane). The parameter calculation method is as follows: the channel length uses the polyline accumulation distance formula; the average width uses the diagonal length of the minimum bounding box of the channel cross-section; the advantage direction uses the spatial vector angle calculation, with the angle unit in degrees. The final generated feature data table contains five items: channel number, spatial coordinate sequence, length value, width value, and angle value.

[0084] The feature data post-processing includes data verification and format standardization. In the pressure conduction efficiency feature, values beyond the reasonable range of geology (for example, the spatial variation rate value is greater than 1000 Pa per meter per hour) are corrected, and the correction value is taken as the 95% quantile value of historical data. In the thermal breakthrough advantage path feature, the channel length unit is uniformly converted to meters, and the numerical value is rounded to one decimal place; the probability value is converted to an integer percentage. All feature data is added with a quality identifier: when the data is directly from the monitoring point, it is identified as A level; when it is from interpolation calculation, it is identified as B level. Feature storage uses binary format, and the file header includes feature type identifier, grid size parameter, and time range marker.

[0085] A version management mechanism is established when the feature data is applied. Each generated feature data set is assigned a version number, and the version number coding rule is: year-month-day+serial number (for example, 2024052001). Historical version data is retained for at least three years and stored in a dedicated archive database. The data calling interface provides version query function, and the query conditions include spatial range, time range, and quality level. When the pressure conduction efficiency feature is called, the three-dimensional data body of the current version is returned; when the thermal breakthrough advantage path feature is called, the channel topology relationship diagram is additionally returned.

[0086] Figure 2 The flowchart of the present application for establishing the quantification rule of thermal reservoir resource schedulability is given, which cooperates to analyze the matching degree of pressure conduction efficiency feature and response time requirement, and the coupling relationship of thermal breakthrough advantage path feature and heat supply continuity requirement, establishes the quantification rule of thermal reservoir resource schedulability, and the specific implementation is:

[0087] When the pressure conduction efficiency feature and the response time requirement are dynamically matched and compared, the conversion relationship between the conduction efficiency and the response time is established first in the specific implementation. The spatial variation rate value (unit: Pa per meter per hour) in the pressure conduction efficiency feature is converted into the pressure wave propagation speed, and the conversion relationship is that the propagation speed is equal to the reciprocal of the spatial variation rate value multiplied by the fluid compression coefficient. The fluid compression coefficient is obtained through the core experiment analysis report (for example, the value is 5×10 -9 The maximum allowed response delay time (for example, 120 seconds) of the response time requirement is taken as the matching reference value. The matching comparison operation calculates the theoretical propagation time of the pressure wave from the injection position to the target production position, and the propagation time is equal to the distance between the wells divided by the propagation speed, and the distance data is from the well coordinate database. When the theoretical propagation time is less than or equal to the maximum allowed response delay time, the matching success state identifier is output; when the theoretical propagation time is greater than the maximum allowed response delay time, the matching failure state identifier is output. The matching comparison result is stored as a state code data record, and the state code 0 represents matching success, and the state code 1 represents matching failure, and the state code is accompanied by the actual propagation time value (unit: seconds).

[0088] In analyzing the spatial distribution characteristics of the fluid advantage migration path in the heat breakthrough advantage path feature, the spatial coordinate sequence data in the feature data table is read in implementation. Each fluid advantage migration path is composed of an ordered three-dimensional coordinate point sequence (for example, path P001: point sequence {(100.2, 205.3, -1200.5), (102.5, 208.1, -1201.2),...}). The spatial distribution characteristics are quantified into three geometric parameters: the total length of the path is calculated by the polyline cumulative distance formula (unit: meters), the path direction angle is calculated by the angle between the line connecting the first and last points and the horizontal plane (unit: degrees), and the average depth of the path is obtained by averaging the depth values of all coordinate points (unit: meters). After parameter extraction, the path feature vector is formed, with the vector elements being [length value, angle value, depth value], and the numerical precision is retained to one decimal place. The vector data is stored in the feature analysis database.

[0089] In comparing the spatial distribution characteristics of the fluid advantage migration path with the spatial position relationship of the heat supply well network, a spatial relationship analysis model is constructed in implementation. The heat supply well network position data is imported from the geographic information system, including a three-dimensional coordinate data table of all heat supply wells. For each fluid advantage migration path, the spatial distance to the nearest heat supply well is calculated using the three-dimensional Euclidean distance formula. The spatial position relationship determination rule is set as follows: when the minimum distance is less than the critical value (for example, 50 meters), it is determined as a high-risk relationship; when the minimum distance is between the critical value and twice the critical value (for example, 50 meters to 100 meters), it is determined as a medium-risk relationship; and when the minimum distance is greater than twice the critical value, it is determined as a low-risk relationship. The determination result generates a spatial relationship table, which includes the path number field, the nearest well number field, the minimum distance value field, and the risk level coding field. Risk level coding 0 represents low risk, 1 represents medium risk, and 2 represents high risk.

[0090] In predicting the heat fluid breakthrough risk based on the spatial position relationship, a risk prediction algorithm is applied in implementation. This algorithm is established based on historical event statistical analysis, and the calculation formula is: risk value = path length x sin(path angle) ÷ minimum distance. The path length is in meters, the path angle is in degrees, and the minimum distance is in meters. The calculation result is a dimensionless value. The risk value is divided into levels according to the numerical interval: risk value less than 1 corresponds to low risk level (coding 0), risk value between 1 and 3 corresponds to medium risk level (coding 1), and risk value greater than 3 corresponds to high risk level (coding 2). The prediction operation generates a risk prediction data table, which includes the path number field, the risk value field, and the risk level coding field, with data precision retained to two decimal places.

[0091] When quantifying the degree of influence of the heat fluid breakthrough risk on the heat supply continuity in combination with the minimum temperature maintenance duration threshold, the degree of influence calculation process is executed in implementation. The minimum temperature maintenance duration threshold (e.g. 7200 seconds) is input as a reference parameter. The actual minimum temperature maintenance duration is obtained through heat supply system simulation: a heat supply pipe network heat transfer model is established, and when simulating a heat fluid breakthrough event, the duration for which the heat supply well temperature drops to a critical value (e.g. 55 degrees Celsius) is recorded. The degree of influence value is calculated as the risk level code multiplied by the temperature maintenance coefficient, which is equal to the actual minimum temperature maintenance duration divided by the minimum temperature maintenance duration threshold. The degree of influence classification criteria: a calculated value less than 0.5 is a slight influence (level 0), 0.5 to 1.0 is a moderate influence (level 1), and greater than 1.0 is a serious influence (level 2). The influence degree vector data is output in the format [path number, influence degree value, influence level code].

[0092] When generating the coupling analysis results representing the risk level, the multi-dimensional risk data is integrated in implementation. The coupling analysis result data structure contains four risk level fields: spatial relationship risk level code field, heat fluid breakthrough risk level code field, heat supply influence degree level code field, and comprehensive risk level code field. The comprehensive risk level code takes the maximum value of the first three level codes, and the calculation logic is: comprehensive risk level code = max (spatial relationship risk level code, breakthrough risk level code, influence degree level code). The result data is stored as a risk analysis result table, which contains a path number field and four risk level fields, and an additional comprehensive rating description field (comprehensive risk level code 0 corresponds to "low risk", 1 corresponds to "medium risk", and 2 corresponds to "high risk").

[0093] When establishing the thermal resource dispatchability quantification rule according to the combined relationship of the matching comparison result and the coupling analysis result, a decision matrix table is constructed in specific implementation. The row dimension of the decision matrix corresponds to the matching comparison result state (state code 0 or state code 1), and the column dimension corresponds to the comprehensive risk level (coding 0, 1 or 2). The matrix cell defines the initial value of the quota allocation coefficient: when the matching comparison result is state code 0 and the comprehensive risk level coding is 0, the allocation coefficient is 0.9; when the matching comparison result is state code 0 and the comprehensive risk level coding is 1, the allocation coefficient is 0.7; when the matching comparison result is state code 0 and the comprehensive risk level coding is 2, the allocation coefficient is 0.5; when the matching comparison result is state code 1 and the comprehensive risk level coding is 0, the allocation coefficient is 0.6; when the matching comparison result is state code 1 and the comprehensive risk level coding is 1, the allocation coefficient is 0.4; and when the matching comparison result is state code 1 and the comprehensive risk level coding is 2, the allocation coefficient is 0.2. The allocation coefficient is optimized and adjusted through the historical case library: select historical operation data records (for example, 20 groups of historical cases), when the actual dispatching demand and the coefficient prediction value deviate more than a certain percentage (for example, 15%), adjust the coefficient value in the deviation direction (for example, increase by 0.05 or decrease by 0.05). The final effective quantification rule is expressed as a set of conditional assignment statements:

[0094] When the matching comparison result state code = 0 and the comprehensive risk level coding = 0, the allocation coefficient = K1; when the matching comparison result state code = 0 and the comprehensive risk level coding = 1, the allocation coefficient = K2; when the matching comparison result state code = 0 and the comprehensive risk level coding = 2, the allocation coefficient = K3; when the matching comparison result state code = 1 and the comprehensive risk level coding = 0, the allocation coefficient = K4; when the matching comparison result state code = 1 and the comprehensive risk level coding = 1, the allocation coefficient = K5; and when the matching comparison result state code = 1 and the comprehensive risk level coding = 2, the allocation coefficient = K6; wherein the coefficients K1 to K6 are optimized values calibrated by historical data (for example, K1 = 0.92, K2 = 0.72, K3 = 0.52, K4 = 0.62, K5 = 0.42, K6 = 0.22). After the rule library is established, verification testing is performed, and the verification sample size is not less than 30% of the historical cases, and the verification pass rate threshold is set to 90%.

[0095] A version control mechanism is established when the quantification rule is applied. A new version identifier is generated each time the rule is updated, and the identifier coding rule is the rule type code plus the date (for example, DQ-20240520). Historical version rules are retained for at least five versions and stored in the rule knowledge base. The rule calling interface provides version selection function, and the latest effective version is applied by default. The rule execution process records detailed operation logs, and the log entries include input parameter snapshots, output results, execution timestamps, and operator identifiers.

[0096] The total resource quantity is divided into a base load power dispatchable quota and a civil heating dispatchable quota by a thermal reservoir resource dispatchability quantification rule, which is implemented as follows:

[0097] When performing the quota allocation operation on the total resource quantity using the thermal reservoir resource dispatchability quantification rule, the total resource quantity value (e.g., 5000 megawatt hours) is first obtained. The total resource quantity is determined by a geothermal reservoir productivity evaluation report, which contains basic parameters such as thermal reservoir volume, rock specific heat capacity, and temperature difference. The quota allocation operation is based on the combined relationship of the matching comparison result and the coupling analysis result, which is mapped to an allocation coefficient by a predefined decision rule. The allocation coefficient determination logic is as follows: when the matching comparison result status code is 0 and the comprehensive risk level code of the coupling analysis result is 0, the allocation coefficient is 0.9; when the matching comparison result status code is 0 and the comprehensive risk level code is 1, the allocation coefficient is 0.7; when the matching comparison result status code is 0 and the comprehensive risk level code is 2, the allocation coefficient is 0.5; when the matching comparison result status code is 1 and the comprehensive risk level code is 0, the allocation coefficient is 0.6; when the matching comparison result status code is 1 and the comprehensive risk level code is 1, the allocation coefficient is 0.4; and when the matching comparison result status code is 1 and the comprehensive risk level code is 2, the allocation coefficient is 0.2. The allocation operation is calculated by the formula: the base load power dispatchable quota value is equal to the total resource quantity value multiplied by the allocation coefficient, and the civil heating dispatchable quota value is equal to the total resource quantity value multiplied by (1 minus the allocation coefficient). The calculation process implements data verification: if the allocation coefficient calculation result is less than 0 or greater than 1, an exception handling program is triggered, and the arithmetic mean of the last 10 allocation coefficients (e.g., 0.65) is called as a replacement value to recalculate.

[0098] When generating a base load power dispatchable quota that meets the response timeliness requirement, data conversion and packaging operations are performed. First, the quota value is divided according to the grid dispatching period (e.g., 15-minute period) using an equal proportion allocation algorithm: the period quota is equal to the total quota multiplied by the period length divided by the total length. Then, a time stamp is added to identify the format from start time to end time (e.g., 2025-03-15T08:00:00 to 2025-03-15T08:15:00). Finally, it is packaged into a structured data packet that can be recognized by the grid dispatching system, and the data packet fields include: power station unique identification code field, quota value field (unit: megawatt), time range field, response timeliness requirement value field (e.g., 120 seconds). Before data transmission, compliance checks are performed: check if the quota value is greater than the minimum technical output threshold of the power station (e.g., 15 megawatts), which is obtained from the technical specification manual of the power station; if it is less than the threshold, it is automatically set to zero; check if the time range is within the valid dispatching period, if it is outside, it is automatically truncated to the nearest valid period.

[0099] When generating the civil heating dispatchable quota that meets the heating continuity requirement, the specific implementation is associated with the operation constraint parameters of the heating system. The quota value is converted into a heat power value (unit: megawatt), and the conversion formula is: heat power equals quota value divided by time window length. The time window length is consistent with the heating system regulation period (for example, 60 minutes). A heating dispatch instruction table is generated, and the instruction table structure includes three necessary fields: heating station registration code field, rated heat power value field, and duration length field. The duration length is set according to the minimum temperature maintenance time threshold (for example, 7200 seconds), and when the duration length is greater than the basic dispatch unit, it is automatically divided into continuous sub-periods (for example, a 7200-second quota is divided into 4 1800-second periods). Before the instruction table is transmitted, a supply-demand balance verification is performed: whether the total heating quota meets the current heat load demand, and the heat load demand value is obtained from the real-time database of the heating system; when the demand value exceeds the quota value, the deviation percentage is recorded and a three-level alarm notification is triggered.

[0100] The quota data storage adopts a dual-channel architecture. The real-time operation database stores the currently effective quota data, and the data fields include a resource type identifier field (power type code or heating type code), a quota value field, a valid start time field, and a state marker field (normal / abnormal). The historical archive database stores historical quota records by natural day partition (for example, March 15, 2025 partition), and each partition includes a complete allocation log. The data query interface provides multi-dimensional retrieval functions, and the retrieval conditions include time range selector, resource type selector, and risk level filter. When retrieving the base load power dispatchable quota, the allocation coefficient calculation process tracking record is returned; when retrieving the civil heating dispatchable quota, the associated minimum temperature maintenance time threshold verification marker is additionally returned.

[0101] The quota allocation process implements full-link audit tracking. Each allocation operation generates an unalterable audit record, and the record items include: input total resource quantity value, matching comparison result status code, coupled analysis result comprehensive risk level code, calculated allocation coefficient value, output base load power dispatchable quota value, output civil heating dispatchable quota value, operation precise timestamp (millisecond level), and operator identity authentication code. The audit record has a storage period of three years and six months, and is stored in a write-protected special storage array. Abnormal operation events (such as allocation coefficient out-of-limit) trigger a complete process snapshot, and the snapshot saves all intermediate variable instantaneous values and call stack information.

[0102] The quota application effect verification is fed back by actual operation monitoring data. For the base load power dispatchable quota, the instruction response time sequence data recorded by the power grid dispatching system (unit: second) is collected to verify whether the maximum response delay meets the response time requirement (for example, the measured delay is less than or equal to 120 seconds). For the civil heating dispatchable quota, the continuous low temperature period data recorded by the temperature sensor at the end of the heating pipe network (unit: second) is collected to verify whether the temperature maintenance time length meets the minimum temperature maintenance time length threshold (for example, the measured time length is greater than or equal to 7200 seconds). The verification result generates a performance evaluation report, which contains the quota achievement rate percentage, the demand deviation absolute value, and the optimization adjustment suggestion. When the achievement rate is lower than the set threshold (for example, 85%) for three consecutive dispatching periods, the quantitative rule parameter re-calibration process is automatically triggered.

[0103] When the heating continuity demand exceeds the civil heating dispatchable quota, the priority transfer protocol of the base load power dispatchable quota to the civil heating dispatchable quota is started, which is implemented as follows:

[0104] When the heating continuity demand and the civil heating dispatchable quota are monitored in real time, the data monitoring channel is established to connect the real-time database system of the heating operation system. The real-time database system updates the heating continuity demand value and the civil heating dispatchable quota value at a fixed time interval (for example, every 10 seconds). The difference between the operation demand and the quota is calculated: the excess value Δ is equal to the heating continuity demand value minus the civil heating dispatchable quota value, both in megawatts. When Δ is less than or equal to 0, state code 0 is generated to indicate the balance state of supply and demand; when Δ is greater than 0, state code 1 is generated to indicate the demand excess state. The monitoring process includes data validity verification: if the demand value exceeds the historical reasonable range (for example, more than 120% of the maximum value in the last 30 days) or the quota value is negative, the data exception flag is triggered and the latest valid data is replaced (for example, the valid value of the last monitoring period is taken). The monitoring result is stored in time sequence form, and the record fields include five items: the precise time stamp field, the heating continuity demand value field, the civil heating dispatchable quota value field, the excess value Δ field, and the state code field.

[0105] The priority transfer protocol is triggered when the heat supply continuity requirement continues to exceed the civil heat supply dispatchable quota. The rule for determining the continuous exceeding is defined in the implementation. The rule contains double judgment conditions: first, the state code 1 needs to appear continuously for a certain number of times (for example, 6 consecutive monitoring periods), and second, the cumulative excess duration reaches a time threshold (for example, 300 seconds). The condition judgment uses a sliding window counting mechanism: a fixed length counting queue is created (for example, the queue length is equal to 6), and each time the state code 1 is monitored, a value of 1 is added to the tail of the queue and the head element of the queue is removed; when the sum of all elements in the queue is equal to the queue length, it is judged as a continuous excess state. The time threshold verification is realized by the duration accumulator: the duration is counted from the first occurrence of state code 1 to the current time, and when the duration exceeds the set threshold, the duration accumulator is triggered. When the double conditions are met at the same time, the priority transfer protocol is triggered, and a protocol start instruction data packet is generated, which contains three parameters: the trigger accurate time stamp field, the current excess value Δ field, and the cumulative excess duration field.

[0106] When performing the real-time transfer operation of the base load power dispatchable quota to the civil heat supply dispatchable quota, the transfer amount value is calculated according to the predetermined rule in the implementation. The transfer amount is equal to the current excess value Δ (for example, Δ is equal to 15 megawatts), but it needs to meet two constraints: the transfer amount cannot exceed the current available amount of the base load power dispatchable quota, and the remaining value of the base load power dispatchable quota after the transfer cannot be lower than the minimum technical output threshold (for example, 10 megawatts, which is derived from the technical specification manual of the power station). The transfer operation is executed atomically under the transaction processing mechanism:

[0107] Base load power dispatchable quota update calculation: the new quota value is equal to the original quota value minus the transfer amount value;

[0108] Civil heat supply dispatchable quota update calculation: the new quota value is equal to the original quota value plus the transfer amount value.

[0109] The update operation process is as follows: first, lock the double-quota data table to prevent concurrent modification, then perform calculation and value update, and finally release the lock and write detailed operation log. The processing rule when the transfer amount exceeds the available amount is: the actual transfer amount takes the current available amount value (for example, the available amount is equal to 8 megawatts, then 8 megawatts are transferred), and the transfer amount truncation mark is recorded in the operation log.

[0110] After the transfer operation is completed, the result verification and system feedback are performed. The verification operation includes three steps: first, recalculate the updated excess value Δnew equal to the continuous heating demand value minus the updated civil heating dispatchable quota value; second, verify whether the updated base load power dispatchable quota still meets the response time requirement constraint (for example, verify whether the power adjustment corresponding to the quota can be completed within 120 seconds); finally, generate a transfer effect report document, which contains seven data fields: the original base load power dispatchable quota value field, the original civil heating dispatchable quota value field, the transfer amount value field, the updated base load power dispatchable quota value field, the updated civil heating dispatchable quota value field, the Δnew value field, and the response time verification result field. The report document is transmitted to the power grid dispatching system and the heating operation system through the message queue system, and the message format uses JSON structured data packaging (for example, containing the key-value pairs "base_power_quota": 85, "heating_quota": 65, "transfer_amount": 15, etc.).

[0111] The protocol termination mechanism is activated dynamically according to the real-time supply and demand relationship changes. When the state code 0 (i.e., Δ is less than or equal to 0) is continuously monitored for a certain number of times (for example, 3 times), a protocol termination instruction data packet is generated. The instruction triggers the quota recovery detection process: if the interval between the current time and the time of the latest transfer operation is less than a set value (for example, 3600 seconds), a reverse transfer operation is performed to return a certain proportion (for example, 50%) of the historical transfer amount from the civil heating dispatchable quota to the base load power dispatchable quota. The reverse transfer operation follows the same constraint rules: the returned amount cannot exceed the current adjustable amount of the civil heating dispatchable quota, and the base load power dispatchable quota value after the return cannot exceed the initial allocation value (the initial allocation value refers to the original quota value before any transfer operation is performed). The protocol full-cycle running state is recorded in the protocol tracking database table, which includes twelve fields: protocol start time field, protocol termination time field, cumulative transfer amount value field, trigger count field, and abnormal event code field.

[0112] The evaluation system realizes the optimization of geothermal resource dispatchability through a modular data flow. The data acquisition module collects geothermal reservoir pressure dynamic monitoring data, tracer migration characteristic data, and power grid and heating system dispatch demand parameters to form an original data basis. The demand identification module analyzes the dispatch demand parameters, extracts the response time requirement of the power grid peak shaving reserve system (such as the maximum allowable response delay time) and the heating continuity requirement of the civil heating system (such as the minimum temperature maintenance time threshold). The feature extraction module processes the original data: based on pressure data, the spatial variation rate is calculated to generate the pressure conduction efficiency feature, and the tracer concentration peak migration trajectory is generated to generate the heat breakthrough advantage path feature. The collaborative analysis module establishes a two-way association: the pressure conduction efficiency feature is matched and compared with the response time requirement in terms of propagation time, and the heat breakthrough path feature is combined with the relationship between the heating well network position to predict the risk of heat fluid breakthrough, and then the matching results and risk levels are fused to construct the geothermal reservoir resource dispatchability quantification rule. The quota allocation module applies the rule to dynamically allocate the total resource quantity into a base load power dispatchable quota and a civil heating dispatchable quota. The dynamic transfer module monitors the difference between the heating demand and the quota in real time, and triggers the atomized quota transfer operation when the demand continues to exceed, and adjusts the double-quota ratio in real time according to the excess value. The modules are connected through data interfaces to form a "data acquisition-feature modeling-rule generation-quota allocation-dynamic adjustment" closed loop, solving the problem of coordinated dispatching of geothermal resources between power peak shaving and people's livelihood heating, and realizing the optimization of resource utilization and the improvement of energy supply reliability.

[0113] The calculations involved in the embodiments are all de-dimensioned numerical calculations, and the preset parameters and threshold values in the calculations are set by a person skilled in the art according to the actual situation.

[0114] It should be noted that the present application can be deployed in the device itself to realize embedded application, or run on PC or other terminal with user interface, so as to meet various hardware environments and use requirements.

[0115] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through wireless or wired transmission. The wired transmission includes optical fiber, twisted pair, coaxial cable, etc. The wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0117] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0118] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, which can be located in one place or distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0119] In addition, each functional module in the various embodiments of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0120] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0121] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0122] Finally: the above is only the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A geothermal thermal reservoir resource evaluation system based on multi-source data, characterized in that, include: The data acquisition module is used to acquire dynamic monitoring data of thermal storage pressure, tracer migration characteristics data, and scheduling demand parameters of energy supply entities; The demand identification module is used to identify the response time requirements of the power grid peak-shaving reserve system and the heating continuity requirements of the civil heating system based on the scheduling demand parameters. The feature extraction module is used to extract pressure transmission efficiency features based on dynamic monitoring data of thermal storage pressure, and to invert thermal breakthrough advantage path features based on tracer transport characteristic data. The collaborative analysis module is used to collaboratively analyze the matching degree between pressure transmission efficiency characteristics and response time requirements, as well as the coupling relationship between thermal breakthrough advantage path characteristics and heating continuity requirements, and to establish quantitative rules for the dispatchability of thermal storage resources. The quota allocation module is used to divide the total resource volume into base load power dispatchable quota and residential heating dispatchable quota through the quantitative rules of thermal storage resource dispatchability; The dynamic transfer module is used to initiate a priority transfer protocol from the base load power dispatchable quota to the civil heating dispatchable quota when the heating continuity demand exceeds the dispatchable quota for civil heating.

2. The geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 1, characterized in that, Acquire dynamic monitoring data on thermal storage pressure, tracer migration characteristics data, and dispatch demand parameters from energy supply entities, including: Dynamic monitoring data of geothermal reservoir pressure is collected by pressure sensors deployed at the geothermal wellhead. Fluorescent tracers were injected into geothermal wells in accordance with standard tracer testing procedures, and tracer migration characteristics data were recorded using monitoring equipment. Obtain dispatch demand parameters, including peak-shaving reserve load response demand records, from the power grid dispatch system; The scheduling demand parameters obtained from the heating operation system include the continuity requirements for residential heating.

3. The geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 2, characterized in that, Based on scheduling demand parameters, the response time requirements of the power grid peak-shaving reserve system and the heating continuity requirements of the residential heating system are identified, including: The maximum allowable response delay time in the peak-shaving reserve load response demand record is analyzed and used as the response time requirement of the power grid peak-shaving reserve system. The minimum temperature maintenance duration threshold in the civil heating continuity requirements is extracted as the heating continuity requirement of the civil heating system.

4. The geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 3, characterized in that, Pressure transmission efficiency characteristics are extracted from dynamic monitoring data of thermal reservoir pressure. Simultaneously, the dominant thermal breakthrough pathway characteristics are retrieved based on tracer transport characteristics data, including: Calculate the dynamic evolution of the pressure change rate with spatial distribution in the dynamic monitoring data of thermal reservoir pressure, and generate pressure conduction efficiency characteristics that characterize the reservoir's conduction capacity. By analyzing the spatial migration probability distribution of tracer concentration peaks in tracer transport characteristic data, thermal breakthrough dominant path characteristics are generated to characterize the dominant fluid transport path.

5. A geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 4, characterized in that, Generate pressure transmission efficiency characteristics, including: The rate of pressure change at each spatial location is calculated based on dynamic monitoring data of thermal storage pressure. Analyze the spatial distribution evolution of pressure change rate in the three-dimensional space of the reservoir; Pressure conduction efficiency characteristics are generated based on the spatial distribution evolution of pressure change rate, and these characteristics directly reflect the spatial heterogeneity of reservoir conduction capacity.

6. The geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 4, characterized in that, Characteristics of the thermal breakthrough advantage path include: Peak tracer concentration data were extracted from tracer transport property data; Track the migration trajectory of tracer concentration peaks in the three-dimensional space of the reservoir; Calculate the spatial migration probability distribution based on migration trajectory; The thermal breakthrough dominant path features are generated based on the spatial migration probability distribution. These features directly characterize the spatial distribution of the fluid's dominant transport path.

7. The geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 4, characterized in that, The study collaboratively analyzes the matching degree between pressure transmission efficiency characteristics and response time requirements, as well as the coupling relationship between thermal breakthrough advantage path characteristics and heating continuity requirements, and establishes quantitative rules for the dispatchability of thermal storage resources, including: The pressure transmission efficiency characteristics are dynamically matched and compared with the response time requirements to determine whether the pressure transmission efficiency characteristics meet the response time requirements and obtain the matching and comparison results. Risk coupling analysis is conducted between the characteristics of the thermal breakthrough advantageous path and the demand for heating continuity to assess the degree of impact of the characteristics of the thermal breakthrough advantageous path on the demand for heating continuity and obtain the coupling analysis results. Based on the combined relationship between the matching comparison results and the coupling analysis results, quantitative rules for the dispatchability of thermal storage resources are established.

8. A geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 7, characterized in that, The logic for obtaining the coupling analysis results is as follows: Analysis of the spatial distribution characteristics of the fluid dominant transport path in the thermal breakthrough dominant path characteristics; Compare the spatial distribution characteristics of the dominant fluid transport path with the spatial location relationship of the heating well network; Predicting thermal fluid breakthrough risks based on spatial location relationships; By combining the minimum temperature maintenance duration threshold in heating continuity requirements, the impact of heat fluid breach risk on heating continuity is quantified. Generate coupled analysis results representing the risk level.

9. A geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 7, characterized in that, The total resource volume is divided into baseload power dispatchable quota and residential heating dispatchable quota based on the quantitative rules for the dispatchability of thermal storage resources, including: The quota allocation operation of the total resource quantity is performed by applying the quantification rule of the schedulability of thermal storage resources. The allocation operation is based on the combination relationship of matching comparison results and coupling analysis results. Generate dispatchable baseload power quotas that meet response time requirements and dispatchable civilian heating quotas that meet heating continuity requirements.

10. A geothermal thermal reservoir resource evaluation system based on multi-source data according to claim 9, characterized in that, When the demand for continuous heating exceeds the dispatchable quota for residential heating, a priority transfer protocol is initiated from the dispatchable quota for baseload electricity to the dispatchable quota for residential heating, including: Real-time monitoring of the comparison between heating continuity demand and the dispatchable quota for residential heating; When the demand for continuous heating continues to exceed the dispatchable quota for residential heating, a priority transfer protocol is triggered. Perform real-time transfer operations from baseload power dispatchable quota to residential heating dispatchable quota, with the transfer amount equal to the excess of heating continuity demand over the residential heating dispatchable quota.

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