Geothermal resource quantity estimation and evaluation method

Abstract

The invention relates to the technical field of geothermal exploration, and particularly discloses a geothermal resource quantity estimation and evaluation method, which comprises the following steps of S1, delimiting a target geothermal area, and establishing a three-dimensional concept geologic model of the target geothermal area based on multi-source data fusion; s2, according to the three-dimensional concept geologic model, the spatial form and parameters of target heat storage are determined, and the parameters comprise the heat storage area, the thickness, the porosity, the rock density and the rock specific heat capacity; s3, based on the three-dimensional concept geologic model and the ground temperature gradient data or the borehole temperature measurement data, calculating the heat storage amount of the target heat storage by adopting a volumetric method; and S4, on the basis of hydrogeological test data, geochemical data and the three-dimensional concept geologic model, the geothermal fluid exploitable quantity of the target heat reservoir is estimated. According to the geothermal resource quantity estimation and evaluation method, the three-dimensional solid model capable of accurately depicting the heat storage heterogeneity and the heat control-water guide structure is constructed, and the problem that a traditional model is excessively simplified is fundamentally solved.

Description

Technical Field

[0001] This invention relates to the field of geothermal exploration technology, specifically a method for estimating and evaluating geothermal resources. Background Technology

[0002] Current geothermal resource assessment technologies face two major bottlenecks: traditional volumetric methods, while simple, are overly simplistic, generalizing complex reservoirs into homogeneous models that rely on empirical parameters and can only estimate static reserves, failing to assess sustainable exploitation potential. On the other hand, dynamic methods based on numerical simulations can simulate the exploitation process, but their reliability is severely limited by the accuracy of the initial conceptual model. Existing modeling methods struggle to fully utilize multi-source exploration data to accurately characterize the three-dimensional structure, heterogeneity, and key heat-controlling and water-conducting structures of reservoirs, resulting in large deviations between the model and geological reality, difficulties in correction, and high uncertainty in prediction results.

[0003] Existing methods present a fundamental contradiction between the coarseness of static estimation and the high requirements of dynamic simulation on the basic model. There is an urgent need for a new method that can deeply integrate geological and geophysical data, construct high-precision three-dimensional geological models, and achieve an integrated and accurate evaluation from static reserves to dynamic sustainability. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for estimating and evaluating geothermal resources, comprising the following steps:

[0005] S1. Delineate the target geothermal area and establish a three-dimensional conceptual geological model of the target geothermal area based on multi-source data fusion;

[0006] S2. Based on the three-dimensional conceptual geological model, determine the spatial morphology and parameters of the target geothermal reservoir, including the reservoir area, thickness, porosity, rock density, and rock specific heat capacity.

[0007] S3. Based on the three-dimensional conceptual geological model and geothermal gradient data or borehole temperature measurement data, calculate the thermal storage capacity of the target thermal reservoir using the volumetric method.

[0008] S4. Based on hydrogeological test data, geochemical data and the three-dimensional conceptual geological model, estimate the exploitable amount of geothermal fluid in the target thermal reservoir;

[0009] S5. Couple the thermal storage capacity with the exploitable geothermal fluid capacity, establish a numerical model of the thermal storage capacity of the target geothermal area using numerical simulation methods, and calibrate and verify the model.

[0010] S6. Based on the corrected numerical model of the geothermal reservoir, different mining schemes are set to simulate and predict the changes in the temperature field and pressure field of the geothermal reservoir and the dynamic changes in geothermal resources under long-term mining conditions, and to complete the resource estimation and sustainability assessment.

[0011] Preferably, the process of establishing a three-dimensional conceptual geological model in step S1 specifically includes:

[0012] S1.1 Data Collection and Interpretation: Collect regional geological maps, borehole lithology logs, geophysical exploration data, seismic reflection profile interpretation results, magnetotelluric sounding data, geothermal field measurement data, and linear structural interpretation results of remote sensing images of the target geothermal area and its surroundings.

[0013] S1.2 Key Interface and Element Identification: Based on the data from step S1.1, identify and determine the key geological interfaces and key geological elements that control the structure of the geothermal system. The key geological interfaces include: the top and bottom plates of the main geothermal reservoir, the bottom boundary of the regional thermal insulation cap, and the top boundary of the deep heat source. The key geological elements include: the main faults or fracture zones that control the upwelling of deep fluids, permeable rock layers or structures that serve as lateral recharge boundaries, and intrusive rock masses that can form local thermal anomalies.

[0014] S1.3 Construction of 3D solid models under multi-source data constraints;

[0015] S1.4 Assigning geological attributes to the model: Based on the three-dimensional solid model constructed in step S1.3, initial geological attributes are assigned to the model units in zones and layers according to lithology, structural location and geophysical inversion parameters. The initial geological attributes include lithology type, initial porosity, initial permeability anisotropy and initial thermal conductivity.

[0016] Preferably, step S1.3 specifically includes the following sub-steps:

[0017] S1.3.1 Establish a hierarchical structural framework: Using the main heat-controlling and water-conducting structures identified in step S1.2 as boundaries, the modeling area is divided into multiple secondary structural blocks. Among them, the primary water-conducting faults that penetrate the caprock and connect the deep heat source with the shallow reservoir are used as the primary boundaries that control the vertical migration of fluids, and the secondary faults or lithological change zones that control the lateral flow of fluids in the reservoir are used as the secondary boundaries.

[0018] S1.3.2 Deterministic Modeling of Layered Interfaces: For key geological interfaces with continuous spatial distribution and many data control points, a deterministic interpolation algorithm is adopted to generate triangular mesh surfaces of the top plate, bottom plate, and bottom boundary of the reservoir based on borehole penetration data and seismic interpretation layer data.

[0019] S1.3.3, Structure-lithology co-progressive stochastic modeling: For areas that are strongly dissected by faults or have drastic changes in lithofacies, a goal-based stochastic simulation method is adopted. First, based on the fault occurrence and geophysical properties, three-dimensional entities of fault zones that conform to geological laws are randomly generated within the structural framework. Second, using the sequential indicator simulation method, the spatial distribution of different lithofacies bodies is simulated within each block segmented by the fault zone.

[0020] S1.3.4 Model Fusion and 3D Entity Generation: The layered interface surface generated in step S1.3.2 is fused with the randomized structural-lithological realization generated in step S1.3.3: The randomly simulated lithological bodies are truncated using the layered surface as a constraint to ensure the correct stratigraphic sequence relationship. At the same time, the entity model of the first-order water-conducting fracture is embedded as an independent unit to form the final unified 3D entity model. This model is composed of 3D volume units with clear boundaries, representing the caprock, thermal reservoir, basement, water-conducting fracture zone unit as an independent modeling unit, and different lithological bodies.

[0021] Preferably, in step S1.3.3, the constraints of the target-based stochastic simulation method include: the dip angle, length and density distribution function of the fracture, and the lithological probability volume generated by Kriging interpolation of well logging data and seismic inversion data.

[0022] Preferably, in step S1.4, the initial permeability assigned to the water-conducting fracture zone unit is set to be the highest in the direction parallel to the fracture direction, the second highest in the direction perpendicular to the fracture surface, and the lowest in other directions, so as to characterize the strong anisotropy of its permeability.

[0023] Preferably, in step S1.2, the method for identifying major faults or fracture zones includes: interpreting the seismic reflection profile as a fault, combining the low-resistivity anomaly zone in the magnetotelluric sounding data and the linear structure in the remote sensing image, and performing multi-evidence overlay analysis and three-dimensional visualization correlation to determine its location, attitude, extension range and its intersection relationship with the thermal reservoir.

[0024] Preferably, in step S2, determining the spatial morphology and parameters of the target thermal reservoir specifically includes:

[0025] Using the aforementioned three-dimensional conceptual geological model, the three-dimensional spatial morphology, distribution area, and thickness of the target thermal reservoir can be directly extracted;

[0026] Based on core experimental data, well logging interpretation data, or regional empirical values, and combined with the lithological zoning in the model, the reservoir units in the three-dimensional conceptual geological model are assigned porosity, rock density, and rock specific heat capacity parameters.

[0027] Preferably, in step S3, the formula for calculating the thermal storage capacity using the volumetric method is:

[0028]

[0029] in, Where n is the thermal storage capacity, and n is the total number of volume elements in the three-dimensional solid model of the thermal storage. Let be the volume of the i-th individual unit. For its rock density, Its specific heat capacity, Its average temperature, As the reference temperature, Its effective thermal storage coefficient.

[0030] Preferably, in step S4, the recoverable amount of geothermal fluid in the target thermal reservoir specifically includes:

[0031] The permeability coefficient, water storage coefficient, and hydraulic conductivity of the thermal reservoir are calculated based on pumping test data, and these are used as the basis for zonal correction of the permeability and water storage coefficient attributes of the corresponding volume units in the three-dimensional conceptual geological model.

[0032] By combining the deep geothermal reservoir temperature estimated by the geochemical temperature scale with the chemical composition of geothermal fluids, the genesis, recharge sources and circulation pathways of geothermal fluids are analyzed.

[0033] Based on the three-dimensional conceptual geological model and its initial geological properties, and using a hydrogeological structure model, the exploitable amount of geothermal fluid in the target geothermal reservoir is determined through trial calculations and optimization under the conditions of setting allowable drawdown and planned mining years, using numerical simulation.

[0034] Preferably, in step S5, the calibration and verification of the model specifically includes:

[0035] Historical data on geothermal fluid water level dynamics, wellhead temperature and water chemistry observations, or short-term production test data, are used as constraints for dynamic model correction.

[0036] By using an automatic history fitting algorithm, the permeability, water storage rate, and boundary flow parameters of the fault zone and lithological body units in the model are adjusted so that the simulated water level and temperature dynamic curves match the observed data within the set error range.

[0037] The fitted model was validated using independent observation data sets that were not involved in the historical data fitting, thus confirming the model's reliability.

[0038] This invention provides a method for estimating and evaluating geothermal resources. It has the following beneficial effects:

[0039] By integrating multi-source data and employing a hierarchical construction framework and hybrid modeling techniques, a three-dimensional solid model was constructed that can accurately characterize the heterogeneity of geothermal reservoirs and the heat-controlling and water-conducting structures. This fundamentally overcomes the problem of oversimplification in traditional models, achieving seamless integration of refined resource quantity accounting and dynamic mining simulation. This significantly improves estimation accuracy and prediction reliability. The integrated workflow not only ensures logical consistency throughout the entire process from static assessment to dynamic development scheme optimization, but also provides quantitative early warning of development risks through multi-scenario simulation. As a result, it outputs a practical comprehensive evaluation that combines resource potential, sustainable mining schemes, and economic analysis, providing reliable technical support for the scientific development and efficient management of geothermal resources. Detailed Implementation

[0040] The technical solutions in the embodiments of the present invention have been clearly and completely described. 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.

[0041] In a first embodiment, the present invention provides a technical solution: a method for estimating and evaluating geothermal resources, comprising the following steps:

[0042] S1. Establish a three-dimensional conceptual geological model:

[0043] S1.1 Data Collection and Interpretation:

[0044] Collect and organize the following multi-source data:

[0045] Geological data: 1:50,000 regional geological map, completion reports of 15 exploration and production wells in the area, core descriptions and logging curves (gamma, resistivity, sonic transit time, etc.);

[0046] Geophysical data: Interpretation results (strata, faults) of a 2D seismic network covering the entire region (30 lines in total), 50 magnetotelluric (MT) sounding points, and resistivity profiles obtained through inversion;

[0047] Geothermal fluid data: long-term water level observation data from 8 wells, data from 5 multi-well pumping tests, geochemical full analysis data from all wells (major ions, SiO2, inert gases, etc.), and system temperature measurement data from 10 wells.

[0048] Remote sensing data: Landsat-8 multispectral imagery, used for linear structure interpretation;

[0049] All the above data were imported into the Petrel geological modeling software platform, the coordinate system and reference surface were unified, the seismic profile was reinterpreted, the top and bottom reflection layers (T1, T2) of the thermal reservoir (lower Paleozoic carbonate rocks) were marked, the MT low resistivity anomaly zone (resistivity <10Ω·m) was visualized in three dimensions, and fluid enrichment areas were identified.

[0050] S1.2 Key Interface and Element Identification:

[0051] Key interfaces: T1 is identified as the top plate of the geothermal reservoir (depth 800-1500 meters), and T2 is identified as the bottom plate of the geothermal reservoir (depth 1500-2500 meters). The overlying thick mudstone is the regional caprock. Based on the seismic velocity and geothermal heat flow value, the depth of the top boundary of the deep heat source (granite body) is estimated to be about 5000 meters.

[0052] Key elements: Three primary fractures (F1, F2, F3) trending NE, cutting through the caprock and extending to the basement were identified and determined as the main water-conducting structures. A secondary fracture network trending NW was also identified as an inner lateral seepage channel within the geothermal reservoir.

[0053] S1.3 Construction of 3D solid model under multi-source data constraints:

[0054] Establish a hierarchical structural framework: using F1, F2, and F3 as primary boundaries, the model region is divided into three main structural blocks, and further subdivided using dense secondary fractures as secondary boundaries;

[0055] Deterministic modeling of layered interfaces: Using the drilling depths of 15 wells and the T1 and T2 layers interpreted by seismic analysis as hard data, a kriging interpolation algorithm with fault constraints is used to generate smooth triangular mesh surfaces of the reservoir top and bottom.

[0056] Structural-lithological co-stochastic modeling:

[0057] Fracture modeling: Using the "Target-based random modeling" module, input the attitude (dip angle 65-75°), extension length and inferred fracture zone width (50-100 meters) of F1, F2 and F3 as parameters, and randomly generate 10 three-dimensional fracture zone entities that conform to the distribution of these parameters in the software.

[0058] Lithological modeling: Using well logging interpretation of lithofacies (porous dolomite, dense limestone, and argillaceous interlayers) data, combined with the wave impedance attribute volume obtained from seismic inversion, a sequential indicator simulation method is adopted to simulate the three-dimensional spatial distribution of the three lithofacies in each block divided by faults, generating 10 lithofacies models with equal probability.

[0059] Model fusion and 3D entity generation: Select the random implementation with the highest matching degree with seismic attributes, fuse the deterministic top and bottom plate surfaces with the lithofacies model, use the top and bottom plate surfaces to trim the lithofacies body to ensure the accuracy of the vertical range of the model, and "embed" the generated F1 fault zone entity as an independent high-permeability unit into the model. Finally, a unified 3D entity model is generated, consisting of caprock mudstone body, geothermal reservoir lithofacies body (containing dolomite, limestone, and interlayers), F1 fault zone entity and basement. The model is discretized into approximately 2 million hexahedral mesh elements.

[0060] S1.4 Assigning geological attributes to the model:

[0061] Based on core laboratory data, the "dolomite" unit was given an initial porosity of 8-12%, an initial permeability of 10-50 mD, and a thermal conductivity of 2.5 W / (m·K).

[0062] The "limestone" unit is given a porosity of 2-5%, a permeability of 1-10 mD, and a thermal conductivity of 2.2 W / (m·K);

[0063] The F1 fault zone was given a strong anisotropic permeability, with the strike direction (Kmax) set to 5000mD, the dip direction (Kint) set to 500mD, and other directions (Kmin) set to 50mD, to simulate its characteristics as a high-speed channel.

[0064] The initial temperature field is based on temperature measurement data and is extrapolated to each grid cell using the geothermal gradient (35°C / km);

[0065] S2. Determine the target thermal reservoir space morphology and parameters:

[0066] The unit set marked "dolomite" and some high-porosity "limestone" is directly extracted from the three-dimensional solid model generated in step S1 as an effective thermal reservoir. The software automatically calculates its total volume to be approximately 120 km³. The model already carries parameters such as porosity, density (2650 kg / m³), and specific heat capacity (0.9 kJ / (kg·K)) assigned in step S1.4.

[0067] S3. Calculate heat storage capacity:

[0068] The volume method formula is used for calculation, with each grid cell as the calculation unit (i):

[0069]

[0070] in, (Effective thermal reservoir coefficient) is taken as 80% of porosity. Calculations show that the total heat stored in the target area's thermal reservoir... Approximately Joules (equivalent to the heat equivalent of 120 million tons of standard coal).

[0071] S4. Estimate the recoverable amount of geothermal fluids:

[0072] Using pumping test data from 5 wells, the parameters were back-calculated using the Theis formula, and the average permeability of the thermal reservoir was found to be approximately 20 mD, which is basically consistent with the initial value range of the model. This data was then used for local fine-tuning.

[0073] Geochemical analysis indicates that the geothermal fluids are caused by deep circulation heating of atmospheric precipitation, and the deep geothermal reservoir temperature estimated by the SiO2 temperature scale is 165°C.

[0074] Using the three-dimensional conceptual geological model established in step S1 as the hydrogeological structure, it was imported into the TOUGH2 geothermal numerical simulation software. Based on the existing production well locations, a 30-year mining period was simulated, and the maximum water level drawdown was controlled to not exceed 500 meters (to prevent scaling and casing damage). Through repeated adjustments to the total mining flow rate, the calculation was finally determined that under the constraints, the sustainable exploitable amount of geothermal fluid in this geothermal field is approximately 8 million cubic meters per year.

[0075] S5. Establish and calibrate the numerical model of the thermal reservoir:

[0076] By integrating the thermal storage capacity (as the initial energy condition) in step S3 with the boundary conditions and mining scheme in step S4, a numerical model of non-isothermal multiphase flow thermal storage is established.

[0077] Model calibration: The dynamic data of water level and temperature from three observation wells over the past five years were selected as the fitting target. An automatic historical fitting program (such as PEST) was run to mainly adjust the permeability of the F1 fault zone and its affected area, as well as the lateral recharge flux of the model. After 15 iterations, the root mean square error between the simulated water level and the measured water level was less than 10 meters, and the temperature error was less than 3°C, indicating a good fitting effect.

[0078] Model validation: Data from two other observation wells not included in the fitting were used for validation over the past year. The simulation results were basically consistent with the trend and magnitude of the observed data, confirming the high reliability of the model.

[0079] S6. Long-term mining simulation and sustainability assessment:

[0080] Based on the corrected model, three mining schemes are designed:

[0081] Option A: Extraction only, no reinjection, total flow rate of 8 million cubic meters per year;

[0082] Option B: Extract 8 million cubic meters per year, with a 50% recharge rate within the same stratum;

[0083] Option C: Extract 10 million cubic meters per year, with a same-layer recharge rate of 80%;

[0084] Simulation predictions for 30 years:

[0085] Option A: In the 15th year, the water temperature of the observation well closest to the main production area drops by more than 10°C (thermal breakthrough); in the 25th year, the local pressure drops below the boiling point pressure, posing a risk of flash evaporation.

[0086] Option B: The pressure field remains stable, the temperature drop of the main fluid is less than 5°C by the end of 30 years, and the resource recovery rate is 35% higher than that of Option A;

[0087] Option C: High initial thermal energy output, but the layout of reinjection wells needs to be optimized to prevent early thermal breakthrough;

[0088] Evaluation conclusion: Option B is the best in terms of resource sustainability and engineering safety, and is recommended as the benchmark development option;

[0089] S7. Comprehensive Evaluation and Report Output:

[0090] Under calculation scheme B, the total recoverable heat over 30 years is The project has a potential power generation capacity of approximately 58MW (based on a 10% thermoelectric conversion efficiency). Considering drilling costs and power plant investment, the project is economically feasible. The final report classifies the resource as proven and recommends a well deployment model of "decentralized production and centralized reinjection". It also points out that the areas on both sides of the F1 fault zone are priority areas for deploying production wells.

[0091] Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art and related fields based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention, unless otherwise specified or limited, shall be implemented according to conventional means in the art.

Claims

1. A method for estimating and evaluating geothermal resources, characterized in that, Includes the following steps: S1. Delineate the target geothermal area and establish a three-dimensional conceptual geological model of the target geothermal area based on multi-source data fusion; S2. Based on the three-dimensional conceptual geological model, determine the spatial morphology and parameters of the target geothermal reservoir, including the reservoir area, thickness, porosity, rock density, and rock specific heat capacity. S3. Based on the three-dimensional conceptual geological model and geothermal gradient data or borehole temperature measurement data, calculate the thermal storage capacity of the target thermal reservoir using the volumetric method. S4. Based on hydrogeological test data, geochemical data and the three-dimensional conceptual geological model, estimate the exploitable amount of geothermal fluid in the target thermal reservoir; S5. Couple the thermal storage capacity with the exploitable geothermal fluid capacity, establish a numerical model of the thermal storage capacity of the target geothermal area using numerical simulation methods, and calibrate and verify the model. S6. Based on the corrected numerical model of the geothermal reservoir, different mining schemes are set to simulate and predict the changes in the temperature field and pressure field of the geothermal reservoir and the dynamic changes in geothermal resources under long-term mining conditions, and to complete the resource estimation and sustainability assessment.

2. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, The process of establishing a three-dimensional conceptual geological model in step S1 specifically includes: S1.1 Data Collection and Interpretation: Collect regional geological maps, borehole lithology logs, geophysical exploration data, seismic reflection profile interpretation results, magnetotelluric sounding data, geothermal field measurement data, and linear structural interpretation results of remote sensing images of the target geothermal area and its surroundings. S1.2 Key Interface and Element Identification: Based on the data from step S1.1, identify and determine the key geological interfaces and key geological elements that control the structure of the geothermal system. The key geological interfaces include: the top and bottom plates of the main geothermal reservoir, the bottom boundary of the regional thermal insulation cap, and the top boundary of the deep heat source. The key geological elements include: the main faults or fracture zones that control the upwelling of deep fluids, permeable rock layers or structures that serve as lateral recharge boundaries, and intrusive rock masses that can form local thermal anomalies. S1.3 Construction of 3D solid models under multi-source data constraints; S1.4 Assigning geological attributes to the model: Based on the three-dimensional solid model constructed in step S1.3, initial geological attributes are assigned to the model units in zones and layers according to lithology, structural location and geophysical inversion parameters. The initial geological attributes include lithology type, initial porosity, initial permeability anisotropy and initial thermal conductivity.

3. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, Step S1.3 specifically includes the following sub-steps: S1.3.1 Establish a hierarchical structural framework: Using the main heat-controlling and water-conducting structures identified in step S1.2 as boundaries, the modeling area is divided into multiple secondary structural blocks. Among them, the primary water-conducting faults that penetrate the caprock and connect the deep heat source with the shallow reservoir are used as the primary boundaries that control the vertical migration of fluids, and the secondary faults or lithological change zones that control the lateral flow of fluids in the reservoir are used as the secondary boundaries. S1.3.2 Deterministic Modeling of Layered Interfaces: For key geological interfaces with continuous spatial distribution and many data control points, a deterministic interpolation algorithm is adopted to generate triangular mesh surfaces of the top plate, bottom plate, and bottom boundary of the reservoir based on borehole penetration data and seismic interpretation layer data. S1.3.3, Structure-lithology co-progressive stochastic modeling: For areas that are strongly dissected by faults or have drastic changes in lithofacies, a goal-based stochastic simulation method is adopted. First, based on the fault occurrence and geophysical properties, three-dimensional entities of fault zones that conform to geological laws are randomly generated within the structural framework. Second, using the sequential indicator simulation method, the spatial distribution of different lithofacies bodies is simulated within each block segmented by the fault zone. S1.3.4 Model Fusion and 3D Entity Generation: The layered interface surface generated in step S1.3.2 is fused with the randomized structural-lithological realization generated in step S1.3.3: The randomly simulated lithological bodies are truncated using the layered surface as a constraint to ensure the correct stratigraphic sequence relationship. At the same time, the entity model of the first-order water-conducting fracture is embedded as an independent unit to form the final unified 3D entity model. This model is composed of 3D volume units with clear boundaries, representing the caprock, thermal reservoir, basement, water-conducting fracture zone unit as an independent modeling unit, and different lithological bodies.

4. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, In step S1.3.3, the objective-based stochastic simulation method includes the following constraints: the dip angle, length, and density distribution function of the fracture, and the lithological probability volume generated by Kriging interpolation of well logging data and seismic inversion data.

5. The method for estimating and evaluating geothermal resources according to claim 3, characterized in that, In step S1.3.4, the initial permeability assigned to the water-conducting fracture zone unit is set to be the highest in the direction parallel to the fracture direction, the second highest in the direction perpendicular to the fracture surface, and the lowest in other directions, in order to characterize the strong anisotropy of its permeability.

6. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, In step S1.2, the method for identifying major faults or fracture zones includes: interpreting the seismic reflection profile as a fault, combining the low-resistivity anomaly zone in the magnetotelluric sounding data and the linear structure in the remote sensing image, and performing multi-evidence overlay analysis and three-dimensional visualization correlation to determine its location, attitude, extension range and its intersection relationship with the thermal reservoir.

7. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, In step S2, determining the spatial morphology and parameters of the target thermal reservoir specifically includes: Using the aforementioned three-dimensional conceptual geological model, the three-dimensional spatial morphology, distribution area, and thickness of the target thermal reservoir can be directly extracted; Based on core experimental data, well logging interpretation data, or regional empirical values, and combined with the lithological zoning in the model, the reservoir units in the three-dimensional conceptual geological model are assigned porosity, rock density, and rock specific heat capacity parameters.

8. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, In step S3, the formula for calculating the thermal storage capacity using the volumetric method is: in, Where n is the thermal storage capacity, and n is the total number of volume elements in the three-dimensional solid model of the thermal storage. Let be the volume of the i-th individual unit. For its rock density, Its specific heat capacity, Its average temperature, As the reference temperature, Its effective thermal storage coefficient.

9. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, In step S4, the exploitable amount of geothermal fluid in the target geothermal reservoir specifically includes: calculating the permeability coefficient, water storage coefficient, and hydraulic conductivity of the geothermal reservoir based on pumping test data, and using these as the basis for zonal correction of the permeability and water storage properties of the corresponding volume units in the three-dimensional conceptual geological model; By combining the deep geothermal reservoir temperature estimated by the geochemical temperature scale with the chemical composition of geothermal fluids, the genesis, recharge sources and circulation pathways of geothermal fluids are analyzed. Based on the three-dimensional conceptual geological model and its initial geological properties, and using a hydrogeological structure model, the exploitable amount of geothermal fluid in the target geothermal reservoir is determined through trial calculations and optimization under the conditions of setting allowable drawdown and planned mining years, using numerical simulation.

10. The method for estimating and evaluating geothermal resources according to claim 1, characterized in that, In step S5, the calibration and verification of the model specifically includes: Historical data on geothermal fluid water level dynamics, wellhead temperature and water chemistry observations, or short-term production test data, are used as constraints for dynamic model correction. By using an automatic history fitting algorithm, the permeability, water storage rate, and boundary flow parameters of the fault zone and lithological body units in the model are adjusted so that the simulated water level and temperature dynamic curves match the observed data within the set error range. The fitted model was validated using independent observation data sets that were not involved in the historical data fitting, thus confirming the model's reliability.

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