Groundwater reservoir suitability evaluation method and device

Abstract

The present disclosure relates to the technical field of groundwater resource assessment management, and relates to a groundwater reservoir suitability evaluation method and device. The method comprises: constructing a three-dimensional geological model according to drilling stratification information, geological profile information and geophysical prospecting data information of a region to be evaluated; determining a hydrogeological parameter three-dimensional distribution field according to hydrogeological parameters and the three-dimensional geological model; determining an evaluation index system, quantifying the evaluation index of an evaluation grid unit in the three-dimensional geological model to obtain a quantitative index of the evaluation grid unit; discriminating constraint conditions of the evaluation grid unit according to the three-dimensional geological model and the hydrogeological parameter three-dimensional distribution field, masking the three-dimensional geological model according to a discrimination result to obtain a reference geological model; determining the weight of the quantitative index of the evaluation grid unit of the reference geological model, determining a comprehensive suitability index of the evaluation grid unit according to the weight and the quantitative index, grading the reference geological model based on the comprehensive suitability index, and generating a target evaluation model.

Description

Technical Field

[0001] This disclosure relates to the field of groundwater resource assessment and management technology, and more specifically, to a method and apparatus for assessing the suitability of groundwater reservoirs. Background Technology

[0002] Existing methods for assessing the suitability of underground reservoirs are mostly based on two-dimensional planar analysis or single hydrogeological indicators. They typically employ empirical zoning or simple weighted evaluation methods, which fail to accurately reflect the three-dimensional spatial structural characteristics of aquifers and their comprehensive impact on reservoir formation conditions. Especially in alluvial fans, basin edges, and areas with complex geological conditions, aquifer thickness, aquitard continuity, and hydrogeological parameters exhibit significant spatial differences. Traditional evaluation methods are prone to biasing the evaluation results, affecting the scientific validity and engineering safety of underground reservoir site selection.

[0003] It should be noted that the information in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method and apparatus for evaluating the suitability of underground reservoirs, thereby overcoming, to some extent, the deficiencies of related technologies, improving the accuracy of underground reservoir suitability evaluation, and thus improving the accuracy and reliability of underground reservoir site selection and planning.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, a method for evaluating the suitability of underground reservoirs is provided, comprising: constructing a three-dimensional geological model of the area to be evaluated based on borehole stratification information, geological profile information, and geophysical data; the three-dimensional geological model including the spatial distribution, thickness distribution, and boundary conditions of aquifers and impermeable layers; determining the three-dimensional distribution field of hydrogeological parameters of the area to be evaluated based on the hydrogeological parameters and the three-dimensional geological model; determining the evaluation index system corresponding to the area to be evaluated, and performing quantitative calculations on each evaluation grid unit in the three-dimensional geological model to obtain the quantitative index of each evaluation grid unit; for each evaluation grid unit, performing constraint condition discrimination based on the three-dimensional geological model and the three-dimensional distribution field of hydrogeological parameters, and masking the three-dimensional geological model based on the discrimination results to obtain a reference geological model; determining the weight of the quantitative index of each evaluation grid unit in the reference geological model, and determining the comprehensive suitability index of the evaluation grid unit based on the weight and the corresponding quantitative index, and classifying the reference geological model based on the comprehensive suitability index to generate a target evaluation model, which is used to characterize the suitability of underground reservoir construction in the area to be evaluated.

[0007] According to one aspect of this disclosure, a groundwater reservoir suitability evaluation device is provided, comprising: a first model construction module, used to construct a three-dimensional geological model of the area to be evaluated based on borehole stratification information, geological profile information, and geophysical data information of the area to be evaluated, wherein the three-dimensional geological model includes the spatial distribution, thickness distribution, and boundary conditions of aquifers and impermeable layers; a determination module, used to determine the three-dimensional distribution field of hydrogeological parameters of the area to be evaluated based on the hydrogeological parameters of the area to be evaluated and the three-dimensional geological model; and an index system construction module, used to determine the evaluation index system corresponding to the area to be evaluated, and to perform index quantification calculation on each evaluation grid unit in the three-dimensional geological model to obtain the quantified index of each evaluation grid unit; the second... The second model construction module is used to, for each evaluation grid unit, perform constraint condition discrimination based on the three-dimensional geological model and the three-dimensional distribution field of hydrogeological parameters, and perform masking on the three-dimensional geological model according to the discrimination results to obtain a reference geological model. The evaluation module is used to determine the weight of the quantitative index of each evaluation grid unit in the reference geological model, and determine the comprehensive suitability index of the evaluation grid unit according to the weight and the corresponding quantitative index, so as to classify the reference geological model based on the comprehensive suitability index and generate a target evaluation model. The target evaluation model is used to characterize the suitability of underground reservoir construction in the area to be evaluated.

[0008] According to one aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the above methods.

[0009] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the above methods by executing the executable instructions.

[0010] The exemplary embodiment of the groundwater reservoir suitability evaluation method disclosed herein involves: constructing a three-dimensional geological model of the area to be evaluated based on borehole stratification information, geological profile information, and geophysical data; the three-dimensional geological model including the spatial distribution, thickness distribution, and boundary conditions of aquifers and impermeable layers; determining the three-dimensional distribution field of hydrogeological parameters of the area to be evaluated based on the three-dimensional geological model; determining the evaluation index system corresponding to the area to be evaluated, and performing index quantification calculations on each evaluation grid unit in the three-dimensional geological model to obtain the quantified index of each evaluation grid unit; for each evaluation grid unit, based on the three-dimensional geological model and the three-dimensional distribution field of hydrogeological parameters, performing constraint condition discrimination on the evaluation grid unit based on preset constraint conditions, and masking the three-dimensional geological model based on the discrimination results to obtain a reference geological model; determining the weight of the quantified index of each evaluation grid unit in the reference geological model, and determining the comprehensive suitability index of the evaluation grid unit based on the weight and the corresponding quantified index; and performing a graded judgment on the reference geological model based on the comprehensive suitability index to generate a target evaluation model, which is used to characterize the suitability of groundwater reservoir construction in the area to be evaluated. On the one hand, elevating the suitability assessment of underground reservoirs from two-dimensional analysis to a three-dimensional spatial scale allows for precise characterization of spatial differences in aquifer thickness, permeability, and water storage capacity. This avoids the evaluation bias caused by the loss of vertical information in traditional planar assessments, thus improving the accuracy of underground reservoir suitability assessments. On the other hand, through a hard constraint mask based on three-dimensional spatial calculations, it is possible to accurately identify engineering risk locations such as areas with missing impermeable layers and insufficient thickness, thereby mitigating leakage and dam failure hazards at the source. Furthermore, each assessment grid cell retains complete quantitative values ​​of indicators, weight allocations, and intermediate calculation data, thereby improving the reliability of underground reservoir suitability assessments.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0012] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation.

[0013] Figure 1 A diagram illustrating an application environment according to an exemplary embodiment of this disclosure is shown.

[0014] Figure 2 A flowchart of a method for evaluating the suitability of an underground reservoir according to an exemplary embodiment of the present disclosure is shown.

[0015] Figure 3 A flowchart illustrating an implementation of constructing a three-dimensional geological model of a region to be evaluated according to an exemplary embodiment of this disclosure is shown.

[0016] Figure 4 A flowchart illustrating an implementation method of a three-dimensional distribution field of hydrogeological parameters of an area to be evaluated according to an exemplary embodiment of the present disclosure is shown.

[0017] Figure 5 A flowchart illustrating the determination of a reference geological model according to an exemplary embodiment of the present disclosure is shown.

[0018] Figure 6 A flowchart illustrating an implementation method for determining the comprehensive suitability index of an evaluation grid cell according to an exemplary embodiment of the present disclosure is shown.

[0019] Figure 7 A flowchart illustrating an implementation of an updated suitability level for an evaluation grid cell in a target rating model, according to an exemplary embodiment of the present disclosure, is shown.

[0020] Figure 8 A schematic diagram of the composition of an underground reservoir suitability evaluation device according to an exemplary embodiment of the present disclosure is shown.

[0021] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0023] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0025] Existing methods for assessing the suitability of underground reservoirs are mostly based on two-dimensional planar analysis or single hydrogeological indicators. They typically employ empirical zoning or simple weighted evaluation methods, which fail to accurately reflect the three-dimensional spatial structural characteristics of aquifers and their comprehensive impact on reservoir formation conditions. Especially in alluvial fans, basin edges, and areas with complex geological conditions, aquifer thickness, aquitard continuity, and hydrogeological parameters exhibit significant spatial differences. Traditional evaluation methods are prone to biasing the evaluation results, affecting the scientific validity and engineering safety of underground reservoir site selection.

[0026] Based on this, an exemplary embodiment of the present disclosure provides a method for evaluating the suitability of underground reservoirs. By constructing a three-dimensional geological model of underground space, extracting aquifer structure and hydrogeological parameters, establishing a calculable evaluation index system and constraints, and introducing the analytic hierarchy process (AHP) to achieve quantitative evaluation and classification of underground reservoir suitability, thereby improving the accuracy and reliability of underground reservoir site selection and planning.

[0027] It should be noted that the methods of the exemplary embodiments disclosed herein can be applied to scenarios such as water resource regulation and storage, ecological protection and restoration, and comprehensive utilization of water resources, including but not limited to these application scenarios.

[0028] The underground reservoir suitability evaluation method provided in the exemplary embodiments of this disclosure can be applied to, for example... Figure 1 The application environment shown is illustrated. Terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102, or it can be located in the cloud or on another network server.

[0029] In one exemplary embodiment, the provided underground reservoir suitability assessment method can be executed by server 102, and the corresponding underground reservoir suitability assessment device is disposed in server 102. Correspondingly, in this method executed by server 102, server 102 can respond to a triggering command to begin executing the steps in the technical solution of the exemplary embodiment of this disclosure, wherein the triggering command can be sent by a terminal used by a user, or can be triggered locally by the server in response to some automated event.

[0030] Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Server 102 can execute background tasks.

[0031] Furthermore, in another exemplary embodiment, terminal 101 may also have similar functions to server 102, thereby executing the underground reservoir suitability assessment method provided by the exemplary embodiments of this disclosure. Terminal 101 may be a smartphone, tablet, laptop, desktop computer, or Internet of Things (IoT) device. Terminal 101 may also be referred to as a mobile terminal, terminal device, mobile device, etc., and the exemplary embodiments of this disclosure do not limit the type of terminal 101.

[0032] Furthermore, the technical solutions of the exemplary embodiments of this disclosure can also be executed collaboratively by terminal 101 and server 102. In this collaborative execution method, some steps of the technical solutions provided in the exemplary embodiments of this disclosure are executed by terminal 101, while other steps are executed by server 102. In this collaborative execution method, the steps executed by terminal 101 and server 102 respectively can be dynamically adjusted according to actual conditions, and no special restrictions are placed on this. Terminal 101 and server 102 can be directly or indirectly connected via wireless communication, and no special restrictions are placed on this in the exemplary embodiments of this disclosure.

[0033] refer to Figure 2 The diagram shows a flowchart of an exemplary embodiment of the present disclosure of a method for evaluating the suitability of an underground reservoir. Figure 2 As shown, the suitability evaluation method for underground reservoirs includes steps S210 to S250, as detailed below: In step S210, a three-dimensional geological model of the area to be evaluated is constructed based on the borehole stratification information, geological profile information, and geophysical data of the area to be evaluated. The three-dimensional geological model includes the spatial distribution, thickness distribution, and boundary conditions of the aquifer and impermeable layer.

[0034] In an exemplary embodiment of this disclosure, borehole stratification information is vertical sequence record data of underground soil and rock layers obtained through drilling operations. Each borehole stratification information may include, but is not limited to, borehole coordinates, stratification data, and inclination data. Geological profile information refers to interpretive map data laid out along a specific exploration line, reflecting the lateral distribution of underground strata structure, and may originate from measured geological profiles, inferred geological slopes, etc. Geophysical data information refers to indirect measurement data obtained through geophysical exploration methods, reflecting differences in the physical properties of underground soil and rock masses, and may include, but is not limited to, magnetotelluric sounding data, seismic exploration data, and high-density electrical resistivity tomography data. Optionally, the above information of the evaluation area (i.e., the study area) can be collected in various ways, and this disclosure does not limit the specific collection methods.

[0035] The three-dimensional geological model of the area to be evaluated is a digital model constructed by fusing the aforementioned multi-source data using computer three-dimensional modeling technology, reflecting the spatial distribution of the underground stratigraphic structure. In this disclosure, the model not only includes the three-dimensional geometric morphology of the stratigraphic interfaces but also clearly distinguishes the spatial distribution, thickness variations, and lateral and vertical boundary conditions of aquifers and impermeable layers. An aquifer refers to a soil or rock layer capable of storing and transporting groundwater; it can be sand, gravel, or fractured bedrock, possessing high permeability and specific yield. An impermeable layer refers to a soil or rock layer with extremely low permeability that impedes groundwater flow; it can be clay, mudstone, or intact shale. Boundary conditions are the hydrogeological definitions of the lateral and vertical boundaries of the three-dimensional geological model in space. Lateral boundaries include impermeable boundaries and flow boundaries, such as coinciding with regional impermeable faults or lithological boundaries, or contacting rivers or piedmont recharge zones. Vertical boundaries include top and bottom boundaries, such as the stable impermeable slab at the lowest point within the modeling depth range. A three-dimensional geological model can reflect the potential water storage space of an underground reservoir.

[0036] In step S220, the three-dimensional distribution field of the hydrogeological parameters of the area to be evaluated is determined based on the hydrogeological parameters and the three-dimensional geological model of the area to be evaluated.

[0037] In the exemplary embodiments of this disclosure, hydrogeological parameters refer to quantitative indicators characterizing the hydraulic properties of aquifers and the movement characteristics of groundwater, such as permeability coefficient, specific yield, and groundwater level. The three-dimensional distribution field of hydrogeological parameters refers to transforming hydrogeological parameters obtained from discrete points into a three-dimensional continuous data volume that perfectly matches the grid structure of a three-dimensional geological model through spatial interpolation algorithms or parameter inversion algorithms. For example, permeability coefficients obtained from borehole pumping tests and water level data from groundwater monitoring wells are introduced into the three-dimensional geological model, and a three-dimensional distribution field of hydrogeological parameters is formed through spatial interpolation algorithms or parameter inversion algorithms, thereby providing quantitative parameter support for the suitability evaluation of groundwater reservoirs.

[0038] In this three-dimensional distribution field, each evaluation grid cell is assigned a corresponding hydrogeological parameter value, including but not limited to the permeability coefficient, water supply of the grid cell, and groundwater level elevation at the location of the grid cell.

[0039] In step S230, the evaluation index system corresponding to the area to be evaluated is determined, and the index quantification calculation is performed on each evaluation grid unit in the three-dimensional geological model to obtain the quantified index of each evaluation grid unit.

[0040] In the exemplary embodiments of this disclosure, the evaluation index system refers to a set of indicators consisting of multiple interrelated and hierarchically distinct evaluation indicators, used to comprehensively and systematically measure the suitability of groundwater reservoir construction in the area to be evaluated. The selection of indicators in this evaluation index system follows the principles of reservoir formation mechanism relevance, three-dimensional expressibility, data availability, and quantification, ensuring that the evaluation indicators can accurately reflect the water storage, regulation, and operational safety conditions of the groundwater reservoir. The principle of reservoir formation mechanism relevance requires that the indicators must be directly related to the water storage, regulation, and operational safety conditions of the groundwater reservoir; the principle of three-dimensional expressibility requires that the indicators can be quantitatively calculated in each evaluation grid cell in three-dimensional space; the principle of data availability requires that the raw data required for the indicators can be obtained through existing exploration, testing, or monitoring methods; and the principle of quantification requires that the indicators have clear mathematical definitions and calculation formulas to avoid subjective qualitative descriptions.

[0041] The evaluation grid cell refers to the smallest spatial computational unit formed by meshing a three-dimensional geological model, such as a Voxel. Each evaluation grid cell has unique three-dimensional spatial coordinates and carries lithological, geometric, and hydrogeological parameters. This evaluation grid cell is the basic computational unit for performing index quantification calculations, constraint condition discrimination, and comprehensive suitability evaluations. Index quantification calculations refer to performing numerical calculations on each evaluation index within the evaluation index system, for each evaluation grid cell, based on its carried attribute data, to obtain the specific value of that index within that grid cell.

[0042] Optionally, the evaluation index system may include water storage space condition indicators, hydrogeological condition indicators, recharge and hydrodynamic condition indicators, and engineering and ecological condition indicators. Among them, water storage space condition indicators shall at least include the effective thickness and effective water storage volume of the aquifer; hydrogeological condition indicators shall at least include the permeability coefficient and the groundwater level depth; recharge and hydrodynamic condition indicators shall at least include the natural recharge intensity and the groundwater runoff intensity; and engineering and ecological condition indicators shall at least include the aquitard integrity index.

[0043] Specifically, aquifer space condition indicators are used to quantitatively characterize the water storage capacity and spatial scale of underground reservoirs. The thickness of the aquifer determines the vertical height of the underground reservoir's water storage space. The greater the thickness, the greater the potential water output from a single well, and the more abundant the water storage space. The aquifer thickness can be determined by the following formula: Formula 1 Where H is the aquifer thickness, Z top Z is the top interface of the aquifer. bottom The elevation of the bottom interface.

[0044] The effective water storage volume directly characterizes the actual exploitable water volume that an evaluation grid cell can contribute under a unit drawdown condition. A larger value indicates a stronger water storage capacity at that spatial location. The effective water storage volume can be determined by the following formula: Formula 2 Where V is the effective water storage volume, A is the horizontal area of ​​the evaluation grid cell, and μ is the aquifer specific yield.

[0045] Hydrogeological indicators characterize the water conduction and release capacity of aquifers, reflecting the quality of groundwater development and utilization conditions. The permeability coefficient characterizes the seepage capacity of groundwater within an aquifer and can be obtained through pumping test results or empirical formulas. Groundwater level depth is the vertical distance from the surface to the free water surface, reflecting the saturation state and recharge conditions of the aquifer. Too shallow a depth may increase engineering construction difficulties, such as pit water inrush and foundation stability problems, while too deep a depth increases pumping energy consumption and well construction costs. The groundwater level depth can be determined using Equation 3: Formula 3 Where D is the depth of the groundwater level, Z ground Z is the surface elevation. water This refers to the groundwater level elevation.

[0046] The recharge and hydrodynamic condition indicators are used to characterize the natural recharge capacity and water renewal intensity of underground reservoirs, reflecting their sustainable operational potential. Natural recharge intensity is the amount of water entering the aquifer per unit horizontal area per unit time. Natural recharge intensity can be determined based on precipitation infiltration, lateral river recharge, and the water balance results of groundwater numerical models. The methods for obtaining these values ​​are conventional, and this disclosure does not impose any special limitations on them.

[0047] Groundwater flow intensity is the actual flow rate of groundwater within an aquifer, reflecting the rate of groundwater renewal. Groundwater flow intensity can be determined using Equation 4: Formula 4 In the formula, I is the groundwater flow intensity, i is the groundwater gradient, and K is the permeability coefficient.

[0048] Engineering and ecological condition indicators are used to characterize the engineering safety and ecological compatibility of underground reservoir construction, and are key control indicators to ensure the safe operation and environmental friendliness of underground reservoirs. Among them, the aquitard integrity index is a comprehensive index that evaluates the thickness continuity, lithological density, and seepage prevention capacity of the aquitard above the target aquifer, and is used to characterize the seepage prevention and engineering safety conditions of the underground reservoir. The aquitard is the natural roof seepage barrier of the underground reservoir, and its integrity directly affects water storage efficiency, dam safety, and operation and maintenance costs. This index elevates the evaluation of the aquitard from a single thickness judgment to a multi-dimensional comprehensive evaluation including thickness, continuity, and lithology.

[0049] Optionally, the aquitard integrity index can be determined by comprehensively considering the aquitard thickness, continuity, and lithological compactness. For example, based on a three-dimensional geological model, the aquitard grid cells continuously distributed above the target aquitard can be traversed vertically upwards, and their vertical thicknesses accumulated to obtain the continuous thickness of the aquitard. Then, within a horizontal neighborhood window centered on the current evaluation grid cell's planar position, the coefficient of variation of the aquitard thickness for each grid column can be calculated, which can be determined by the ratio of the thickness standard deviation to the mean. Further, the corresponding lithological compactness coefficient can be obtained by querying a preset correspondence based on the aquitard lithology. Finally, the obtained continuous thickness, coefficient of variation, and lithological compactness coefficient of the aquitard are weighted and summed according to preset weights to obtain the aquitard integrity index. The specific preset weights can be flexibly set according to the actual scenario or needs of the area to be evaluated.

[0050] It should be noted that this disclosure may follow the principles of relevance of database formation mechanism, three-dimensional expressibility, data availability, and quantification, and combine practical experience to select indicators to construct the evaluation indicator system for the region to be evaluated.

[0051] In step S240, for each evaluation grid cell, based on the three-dimensional geological model and the three-dimensional distribution field of hydrogeological parameters, the evaluation grid cell is subjected to constraint condition discrimination based on preset constraint conditions, and the three-dimensional geological model is masked according to the discrimination results to obtain a reference geological model.

[0052] In the exemplary embodiments of this disclosure, the preset constraints refer to the rigid engineering safety, water resource, and ecological environment requirements that must be met during the construction of underground reservoirs. Unlike the indicators in the evaluation index system, the constraints do not participate in the weighted scoring but serve as preliminary judgment conditions. Any evaluation grid cell that does not meet any one of the constraints is directly judged as unsuitable for underground reservoir construction, regardless of how superior its other indicators are. This disclosure uses data provided by the three-dimensional geological model and the three-dimensional distribution field of hydrogeological parameters to check whether each evaluation grid cell meets the preset constraints item by item. Masking refers to the operation of specially marking evaluation grid cells that do not meet the constraints in the three-dimensional geological model. The marked grid cells will not participate in the subsequent comprehensive suitability index calculation but will still be retained in the model to ensure the spatial integrity of the output results. Furthermore, the reference geological model refers to the three-dimensional data volume obtained after constraint judgment and masking processing, which adds mask identifier attributes to the original three-dimensional geological model. This model serves as the direct input data for subsequent comprehensive suitability index calculations. Its spatial extent and grid structure are completely consistent with the original three-dimensional geological model. The mask identifier is a state marker attribute attached to the evaluation grid cells that do not meet the constraints.

[0053] In step S250, for each evaluation grid cell in the reference geological model, the weight of the quantitative index of the evaluation grid cell is determined, and the comprehensive suitability index of the evaluation grid cell is determined according to the weight and the corresponding quantitative index. The reference geological model is then graded based on the comprehensive suitability index to generate a target evaluation model. The target evaluation model is used to characterize the suitability of underground reservoir construction in the area to be evaluated.

[0054] In the exemplary embodiments of this disclosure, the weight of the quantitative index of the evaluation grid unit refers to the relative importance coefficient of each evaluation index in the comprehensive suitability evaluation of the underground reservoir. The comprehensive suitability index is a comprehensive quantitative measure of the suitability of the underground reservoir construction of the evaluation grid unit. The grading judgment is based on a preset threshold standard, dividing the continuous numerical range of the comprehensive suitability index into several discrete suitability levels, such as suitable area, relatively suitable area, generally suitable area, and unsuitable area. The target evaluation model is a final three-dimensional data volume generated based on the reference geological model after the comprehensive suitability index calculation and grading judgment. Each evaluation grid unit carries the comprehensive suitability index and suitability level attributes, and can be directly used to guide the planning, design, and spatial layout of underground reservoir projects.

[0055] The underground reservoir suitability assessment method in the exemplary embodiments of this disclosure, on the one hand, elevates the underground reservoir suitability assessment from two-dimensional analysis to a three-dimensional spatial scale, enabling precise characterization of spatial differences in aquifer thickness, permeability, and water storage capacity. This avoids the assessment bias caused by the loss of vertical information in traditional planar assessments, thus improving the accuracy of underground reservoir suitability assessment. On the other hand, through a hard constraint mask based on three-dimensional spatial calculations, it can accurately identify engineering risk locations such as areas with missing impermeable layers and insufficient thickness, avoiding leakage and dam failure hazards at the source. Simultaneously, each assessment grid cell retains complete quantitative values ​​of indicators, weight allocations, and intermediate calculation data, thereby improving the reliability of the underground reservoir suitability assessment.

[0056] The steps are explained in more detail below.

[0057] In one exemplary embodiment, a method for constructing a three-dimensional geological model of the area to be evaluated is provided. For example... Figure 3 As shown, based on borehole stratification information, geological profile information, and geophysical data of the area to be evaluated, constructing a three-dimensional geological model of the area to be evaluated can include: Step S310: Standardize the borehole stratification information, geological profile information, and geophysical data information respectively to obtain standardized borehole stratification information, standardized geological profile information, and standardized geophysical data information.

[0058] Standardization processing involves unifying and standardizing raw geological data from different sources, formats, and coordinate benchmarks, including but not limited to coordinate transformation, standardization of elevation benchmarks, and standardization of lithological names.

[0059] Step S320: Based on the standardized borehole stratification information, generate a three-dimensional trajectory line of the borehole in three-dimensional space, and calibrate the stratification points along the three-dimensional trajectory line to obtain the stratification points of the top plate and bottom plate of the aquifer.

[0060] The 3D trajectory line is a curve reconstructed in three-dimensional space based on the borehole opening coordinates and borehole inclination data. Because the borehole deviates during drilling and is not perfectly vertical, its spatial position needs to be corrected using inclination data to form a 3D line segment that accurately reflects the borehole's orientation—the 3D trajectory line. Layer point calibration involves determining the 3D spatial coordinates of each lithological interface along the borehole's 3D trajectory line, based on borehole layer information (including the top and bottom depths of each lithological layer), and assigning each point a corresponding standard lithological code. For the target aquifer, the focus is on calibrating its top and bottom layer boundaries.

[0061] Specifically, for each borehole, the borehole coordinates and inclination data sequence can be obtained. The minimum curvature method is used to calculate the coordinates of each measuring point and layer point on the borehole trajectory in three-dimensional space. Then, along the three-dimensional trajectory line, based on the top and bottom depths of each layer, the three-dimensional spatial coordinates of each lithological interface are determined, and the standard lithology code and hydrogeological unit type are assigned to the layer point. For the target aquifer, the set of top layer points and the set of bottom layer points are extracted separately.

[0062] Step S330: Using a spatial interpolation algorithm, a three-dimensional surface of the top interface of the aquifer is generated based on the layering points of the top plate of the aquifer, and a three-dimensional surface of the bottom interface of the aquifer is generated based on the layering points of the bottom plate. The spatial distribution and thickness distribution of the aquifer and the impermeable layer are determined according to the spatial positional relationship between the three-dimensional surfaces of the top and bottom interfaces.

[0063] Using known discrete sampling point attribute values, the attribute values ​​of unknown spatial locations are calculated through mathematical models. Kriging interpolation, discrete smoothing interpolation, etc., can be selected according to actual needs, without any special limitations. The top interface three-dimensional surface of the aquifer is a continuous surface in three-dimensional space representing the boundary between the target aquifer group and the overlying impermeable or non-aquifer layer; the bottom interface three-dimensional surface is a continuous surface in three-dimensional space representing the boundary between the target aquifer group and the underlying impermeable or bedrock layer.

[0064] Specifically, the spatial interpolation algorithm can be selected based on the borehole distribution density and geological complexity. For example, when the borehole density is high and the formation changes gently, ordinary kriging interpolation can be used; when the formation has complex structures such as pinch-outs or lenses, discrete smooth interpolation can be used. Then, using the set of top plate stratification points as known sampling points and the coordinates of each point as the interpolation target variable, spatial interpolation is performed to generate a three-dimensional surface of the aquifer top interface. Similarly, using the set of bottom plate stratification points as known sampling points, a three-dimensional surface of the aquifer bottom interface is generated. Finally, based on the three-dimensional surfaces of the top and bottom interfaces, the effective thickness of the aquifer at any planar location within the evaluation area is calculated. This allows us to obtain the distance difference between the three-dimensional surface of the top interface and the corresponding three-dimensional surface of the bottom interface, thereby determining the effective thickness of the aquifer, i.e., obtaining the thickness distribution.

[0065] Step S340: Using the standardized geological profile information and standardized geophysical data, spatial constraints and corrections are applied to the three-dimensional surfaces of the top and bottom interfaces. Lateral and vertical boundary conditions are defined for the three-dimensional spatial structure enclosed by the top and bottom interfaces to obtain the boundary conditions.

[0066] Spatial constraints and corrections to the top and bottom 3D interfaces utilize geological profile and geophysical data as hard constraints. These constraints allow for local adjustments and optimizations of the spatially interpolated top and bottom 3D interfaces, ensuring consistency with known geological knowledge and geophysical information. Lateral boundary conditions define the hydraulic properties of the 3D geological model at its horizontal boundaries, such as impermeable boundaries, flow boundaries, or given head boundaries. Vertical boundary conditions define the hydraulic properties of the 3D geological model at its top and bottom. The top boundary can be a digital elevation model of the surface, receiving precipitation infiltration recharge; the bottom boundary can be the floor of a regionally stable impermeable layer, serving as an impermeable boundary.

[0067] Specifically, for geological profile constraints, standardized geological profile information can be obtained, and the spatial coordinate sequences of the top and bottom boundaries of the target aquifer interpreted on the profile can be extracted. These coordinate sequences are then used as hard constraint points and added to the original set of layered points. Local deformation corrections are then applied to the generated surface to ensure that the 3D surface is consistent with the interpretation boundary in the profile position. For geophysical data constraints, standardized geophysical data information can be obtained for constraint correction. Furthermore, for faults interpreted by geophysical exploration, corresponding fault displacements and faults can be generated in the 3D surface to ensure that the structural morphology of the interface conforms to reality.

[0068] Furthermore, lateral and vertical boundary conditions can be defined for the three-dimensional spatial structure enclosed by the top and bottom three-dimensional surfaces to obtain the boundary conditions.

[0069] Step S350: Based on the spatial distribution, thickness distribution and boundary conditions of the aquifer and impermeable layer, generate a three-dimensional geological model, and perform three-dimensional meshing on the three-dimensional geological model to obtain discretized evaluation mesh units.

[0070] Three-dimensional meshing is the process of discretizing a continuous three-dimensional geological spatial entity into a set of regular or irregular three-dimensional raster units (Voxels) according to a set spatial resolution. Each three-dimensional raster unit is an evaluation mesh unit.

[0071] Specifically, a three-dimensional geological structure solid model is generated using the aforementioned top interface three-dimensional surface, bottom interface three-dimensional surface, and lateral boundary through spatial closed surface stitching technology. This solid model can completely represent the three-dimensional spatial structure of the target aquifer and its upper and lower surrounding rocks. Through three-dimensional meshing, each evaluation mesh cell is assigned attributes, including but not limited to spatial coordinates, lithological properties, geometric properties, and boundary properties.

[0072] This disclosure generates a realistic three-dimensional trajectory line using borehole survey data and marks layer points along the trajectory, avoiding spatial positional deviations caused by directly simplifying boreholes as vertical holes. Furthermore, through interpolation algorithms, discrete borehole points are transformed into continuous three-dimensional surfaces, realizing a three-dimensional continuous representation of the spatial distribution of aquifers. Geological profile interpretation results and geophysical deep information are introduced as constraints into the modeling process to constrain lateral continuity and deep structure, thereby improving the accuracy and reliability of the three-dimensional geological model.

[0073] In one exemplary embodiment, a method for realizing the three-dimensional distribution field of hydrogeological parameters of the area to be evaluated is also provided. For example... Figure 4 Based on the hydrogeological parameters and three-dimensional geological model of the area to be evaluated, the determination of the three-dimensional distribution field of the hydrogeological parameters of the area to be evaluated may include: Step S410: Conduct quality inspections on the hydrogeological parameters to determine the hydrogeological parameters that pass the inspection.

[0074] When verifying the quality of parameters, the reliability of raw hydrogeological parameter data obtained from pumping tests, injection tests, indoor geotechnical tests, and long-term monitoring can be assessed and outliers removed. This ensures that the parameter data used for subsequent spatial interpolation and 3D modeling accurately reflects the hydraulic characteristics of the aquifer, avoiding the contamination of the overall parameter field by spurious data caused by instrument errors, improper test operation, or local interference. For example, the relationship curve between the drawdown of the pumping well and the pumping flow rate can be plotted to verify whether it conforms to a linear relationship. Another example is comparing the experimentally obtained specific yield values ​​with the regional empirical specific yield range for the corresponding lithology; those with excessively large deviations are marked as anomalies, and so on.

[0075] Step S420: Based on the three-dimensional geological model, assign values ​​to each evaluation grid cell in the three-dimensional geological model using the verified hydrogeological parameters.

[0076] Discrete hydrogeological parameter sampling points that have passed quality inspection can be mapped to corresponding evaluation grid cells in a three-dimensional geological model based on their spatial coordinates and sampling depth range. This allows the grid cell to carry the parameter values ​​of the sampling point. Through this step, a spatial correspondence between discrete sampling points and continuous three-dimensional grid space can be established.

[0077] Step S430: Based on the preset lithological constraints, interpolation calculations are performed on each evaluated grid cell after assignment to generate a three-dimensional distribution field of hydrogeological parameters.

[0078] The pre-defined lithological constraint refers to using the lithological properties of each evaluation grid cell in the 3D geological model as the control condition for spatial interpolation. Spatial correlation is only transmitted within the same or closely related lithological cells; spatial correlation across aquitards or lithological abrupt transition interfaces is severed or significantly weakened. This constraint ensures that the parameter field generated by interpolation conforms to sedimentary facies and lithological distribution patterns. In actual interpolation calculations, the attribute values ​​of unknown grid cells can be derived from verified hydrogeological parameters using a mathematical model. For example, interpolation algorithms include, but are not limited to, Kriging interpolation based on lithological constraints and distance-inverse weighted methods. Furthermore, the permeability coefficient, specific yield, and groundwater level data obtained from the above interpolation calculations can be written as new attribute fields into each evaluation grid cell of the 3D geological model.

[0079] Based on this, the three-dimensional distribution field of hydrogeological parameters is consistent with the coordinate system, grid structure, and spatial resolution of the three-dimensional geological model. Each evaluation grid cell carries lithological attributes, geometric attributes, and hydrogeological parameter attributes. Furthermore, the distribution of the parameter field is strictly constrained by lithology and conforms to the laws of geological sedimentation.

[0080] In an exemplary embodiment, the aforementioned preset constraints include at least engineering safety constraints, water resource development constraints, and ecological environment constraints. Based on this, as... Figure 5 For each evaluation grid cell, based on the 3D geological model and the 3D distribution field of hydrogeological parameters, constraint condition discrimination is performed on the evaluation grid cell according to preset constraints. Then, based on the discrimination results, a mask is applied to the 3D geological model to obtain a reference geological model, which may include: Step S510: For the evaluation grid cells, determine whether the thickness of the waterproof layer meets the engineering safety constraints.

[0081] Engineering safety constraints refer to the rigid criteria set to ensure that underground reservoirs do not experience engineering safety accidents such as leakage, roof collapse, or dam instability during construction and operation. In this disclosure, engineering safety constraints are specifically manifested as the minimum requirement for the cumulative thickness of the continuous impermeable layer above the target aquifer. For example, it can be determined whether the thickness of the impermeable layer meets the engineering safety constraint of the following formula: Formula 5 Among them, H c H represents the thickness of the waterproof layer. min The minimum permissible thickness of the waterproof layer.

[0082] Step S520: Based on the three-dimensional distribution field of hydrogeological parameters, determine whether the groundwater at the location of the evaluation grid cell meets the constraints of water resource development.

[0083] Water resource development constraints are rigid criteria set to ensure that the long-term extraction volume of groundwater reservoirs does not exceed the allowable extraction capacity of aquifers and to prevent aquifer depletion or ecological degradation. For example, whether the water resource development volume meets the following water resource development constraints can be determined by the following formula: Formula 6 in, Q represents the amount of water resources developed. allowable The constraint on water resource development can be determined based on water resource assessment or groundwater numerical models.

[0084] Step S530: Based on the three-dimensional distribution field of hydrogeological parameters and the groundwater level elevation at different time points, determine the groundwater level change range corresponding to the evaluation grid unit, so as to judge whether the groundwater level change range meets the ecological environment constraints.

[0085] Ecological and environmental constraints are rigid criteria set to ensure that the dynamic fluctuations in groundwater levels during the operation of underground reservoirs do not exceed the carrying capacity of the ecosystem. This can be determined using the following formula: Formula 7 in, This represents the range of groundwater level changes. This is the ecologically permissible threshold.

[0086] Step S540: Mask the evaluation grid cells that fail to meet at least one of the engineering safety constraints, water resource development constraints, and ecological environment constraints to obtain a reference geological model.

[0087] In practice, evaluation grid cells that do not meet any of the constraints are classified as unsuitable areas for underground reservoirs and will no longer participate in the subsequent suitability index calculation. Specifically, these evaluation grid cells can be masked, meaning that evaluation grid cells that do not meet any of the aforementioned preset constraints are marked with mask attributes.

[0088] This disclosure uses three types of constraints—engineering safety, water resource development, and ecological environment—as prerequisite criteria. Each evaluation grid unit is screened from three dimensions: structural safety, resource sustainability, and ecological protection, to ensure the accuracy and reliability of the obtained reference geological model.

[0089] In one exemplary embodiment, an implementation method for determining the comprehensive suitability index of the evaluation grid cells is also provided. For example... Figure 6 For each evaluation grid cell in the reference geological model, the weights of the quantitative indicators for the evaluation grid cell are determined, and the comprehensive suitability index of the evaluation grid cell is determined based on the weights and the corresponding quantitative indicators, including: Step S610: Construct a hierarchical model, which includes a target layer, a criterion layer, and an indicator layer.

[0090] A hierarchical structure model refers to a hierarchical structure formed by decomposing the decision-making problem of underground reservoir construction suitability evaluation into a hierarchical structure of target layer, criterion layer, and indicator layer. This structure reflects the hierarchical relationship and logical hierarchy among the evaluation factors, enabling hierarchical analysis. Specifically, the target layer characterizes the overall suitability of underground reservoir construction; the criterion layer includes water storage space conditions, hydrogeological conditions, recharge and hydrodynamic conditions, and engineering and ecological conditions; and the indicator layer includes multiple evaluation indicators belonging to each criterion layer.

[0091] Step S620: Based on the hierarchical structure model, construct the judgment matrix corresponding to each layer, and calculate the local weight of the evaluation index in each layer according to the judgment matrix, so as to obtain the global weight of the corresponding evaluation index by weighting the local weights.

[0092] In a hierarchical model, the judgment matrix is ​​a pairwise matrix constructed by comparing the importance of factors at the same level relative to a factor at the next higher level using a 1-9 scale. The element Sij in the judgment matrix represents the importance scale of factor Si relative to factor Sj.

[0093] Local weights refer to the weight allocation of each factor within a certain level relative to its direct superior factor. For example, the weight of each indicator in the indicator layer relative to its corresponding criterion is a local weight. Local weights are obtained by solving for the eigenvectors of the judgment matrix at that level and then normalizing them. Global weights are the final weight values ​​of each evaluation indicator in the indicator layer relative to the overall target layer. Global weights are obtained by multiplying the local weight of the indicator by the criterion layer weight of its corresponding criterion, and the sum of the global weights of all indicators is 1.

[0094] Specifically, a hierarchical model is constructed. The first layer assesses the overall suitability of underground reservoir construction. The second layer includes four evaluation dimensions. The third layer includes specific evaluation indicators belonging to the second-layer criteria, such as effective aquifer thickness, effective storage volume, permeability coefficient, groundwater level depth, and natural recharge intensity. When constructing the judgment matrix for the criterion layer, the importance of the four factors in the criterion layer relative to the first layer can be compared pairwise using a 1-9 scale. Based on this, the judgment matrix for the criterion layer is constructed, and local weights are calculated from the judgment matrix. These local weights are then weighted to obtain the global weight of the corresponding evaluation indicator.

[0095] Step S630: Traverse the reference geological model, screen out the effective evaluation grid cells that are not masked, and for each effective evaluation grid cell, calculate the comprehensive suitability index of the effective evaluation grid cell by weighted summation based on the global weight and the quantitative index of the effective evaluation grid cell.

[0096] An effective evaluation grid cell refers to an evaluation grid cell that is not masked during the constraint condition discrimination step. The comprehensive suitability index of an effective evaluation grid cell is a comprehensive quantitative measure of the suitability of groundwater reservoir construction for each effective evaluation grid cell. Its value is the weighted sum of the standardized values ​​of each evaluation index and their corresponding global weights. For example, the comprehensive suitability index of an effective evaluation grid cell can be determined using the following formula: Formula 8 Where S is the comprehensive suitability index for effectively evaluating grid cells. For global weights, i Standardized values ​​for evaluation indicators.

[0097] This disclosure integrates four major categories of indicators: water storage space, hydrogeology, water supply dynamics, and engineering ecology. It achieves a layer-by-layer aggregation from specific indicators at the bottom level to comprehensive suitability at the top level, which can accurately reflect the relative contribution of each indicator in the overall evaluation system and enable the evaluation results to comprehensively reflect the multi-dimensional conditions for the construction of underground reservoirs.

[0098] In an exemplary embodiment, before calculating the local weights of the evaluation indicators in each layer and weighting the local weights to obtain the global weights of each evaluation indicator, a consistency check can be performed on each judgment matrix, and the target matrix elements in the judgment matrices that fail the check can be adjusted until the judgment matrix passes the consistency check.

[0099] Consistency verification, in the context of hierarchical analysis, is a process of logically verifying the consistency of the constructed judgment matrix. It quantifies and evaluates whether pairwise comparisons within the judgment matrix contain logical contradictions, ensuring the rationality and reliability of the weight vectors derived from the judgment matrix. For example, it checks whether there are erroneous conclusions such as A being more important than B, B being more important than C, but C being more important than A. The consistency of the judgment matrix can be measured by the consistency ratio (CR). When the consistency ratio (CR) is less than 0.1, the judgment matrix passes the consistency test. The consistency ratio can be determined by the ratio of the consistency index to the random consistency index. This consistency index can be calculated using the largest eigenvalue of the judgment matrix and its order; the random consistency index can be obtained through numerous randomized simulation experiments. The target matrix element refers to the individual scale value identified as contributing the most to inconsistency in a judgment matrix that fails the consistency test. This element is the specific object of the adjustment operation and can be identified based on the deviation matrix, without special limitations. The adjustment direction and scale can be determined based on the relationship between the deviation corresponding to the target matrix element and the preset weight ratio, and can be based on the correspondence between the preset magnitude relationship and the adjustment strategy (adjustment direction and adjustment scale). After adjusting the elements of the target matrix, the judgment matrix can be updated accordingly until the consistency check is passed.

[0100] Optionally, if after multiple adjustments the adjusted weight ranking deviates significantly from the original expert judgment, or if the automatic adjustment fails to converge, the adjustment suggestions should be fed back to the user for readjustment.

[0101] This disclosure quantifies and verifies each judgment matrix by calculating the consistency ratio, which can effectively identify logical contradictions that may exist in the pairwise comparison process. Only the judgment matrix that passes the consistency verification has a reasonable weight vector, and the subjectivity of the weight is reduced. This ensures the scientific nature of the weight allocation from the source and provides support for subsequent suitability evaluation.

[0102] In one exemplary embodiment, a specific implementation method for generating a target evaluation model is also provided. This method involves classifying and determining the reference geological model based on a comprehensive suitability index, and generating the target evaluation model may include: First, all evaluation grid cells in the reference geological model are traversed. If the current evaluation grid cell has been masked, it is determined to be of an unsuitable level.

[0103] Secondly, if the current evaluation grid cell is not masked, the suitability level of the current evaluation grid cell is determined according to the comprehensive suitability index corresponding to the current evaluation grid cell and the preset suitability grading rules.

[0104] Finally, the determined suitability levels are added as grading attributes to the evaluation grid cells corresponding to the reference geological model to obtain the target evaluation model.

[0105] The grading determination involves dividing the comprehensive suitability index within a continuous numerical range into several discrete suitability levels based on preset threshold standards. These preset suitability grading rules are predefined threshold interval standards used to map the comprehensive suitability index to suitability levels. These rules can be expressed as piecewise functions or interval lookup tables and can be customized according to the actual engineering needs and hydrogeological conditions of the area to be evaluated. Grading attributes are attribute fields added to each evaluation grid cell to identify its suitability level. These attributes can be stored as integer values ​​(e.g., 1, 2, 3, 4 corresponding to levels I to IV respectively) or strings (e.g., level I, suitable area), facilitating visualization rendering and spatial querying. The target evaluation model refers to the final three-dimensional data volume generated based on the reference geological model after comprehensive suitability index calculation and grading determination, where each evaluation grid cell carries both the comprehensive suitability index and suitability level attributes.

[0106] The target evaluation model generated by this disclosure fully retains the three-dimensional spatial framework and complete evaluation data, which can be directly used for engineering decision-making, thereby facilitating the practical application of underground reservoir suitability evaluation.

[0107] In one exemplary embodiment, an implementation method for updating the suitability level of the evaluation grid cells of the target rating model is also provided. For example... Figure 7 After classifying and determining the reference geological model based on the comprehensive suitability index and generating the target evaluation model, this disclosure also includes: Step S710: Obtain a supply condition dataset for multiple scenarios, wherein the multiple scenarios include at least a high water level scenario, a normal water level scenario, and a low water level scenario.

[0108] The multi-scenario recharge condition dataset is constructed based on data from climate change prediction, land use change prediction, and water resource planning. It consists of multiple sets of spatiotemporal series data on groundwater recharge intensity under different future precipitation, evaporation, and artificial recharge conditions. The wet scenario can be understood as precipitation significantly higher than the multi-year average, the normal scenario is precipitation close to the multi-year average, and the dry scenario is precipitation significantly lower than the multi-year average.

[0109] Step S720: Using the target evaluation model as the spatial framework and the three-dimensional distribution field of hydrogeological parameters as the parameter input, construct a numerical simulation model for unsteady flow groundwater.

[0110] A numerical simulation model for unsteady-state groundwater flow refers to a numerical model capable of simulating the dynamic changes of groundwater flow field over time, and outputting groundwater level, flow velocity, and water volume balance terms for each simulation time step. Using the target evaluation model as the spatial framework, this model is constructed with the three-dimensional distribution field of hydrogeological parameters as input parameters.

[0111] Step S730: Input the recharge condition dataset as boundary conditions into the unsteady flow groundwater numerical simulation model, perform long-term unsteady flow simulations under various scenarios, and obtain the groundwater level change sequence of each evaluation grid cell of the target evaluation model under different simulation time steps.

[0112] Long-period unsteady flow simulation sets the simulation time span to a relatively long time scale, such as 3, 5, 10, or 20 years, with monthly or yearly time steps, to continuously simulate the temporal evolution of the groundwater flow field under given recharge conditions and extraction schemes. Through the simulation process, for each evaluation grid cell, a time-sequential sequence of groundwater level elevation values ​​can be extracted from the unsteady flow simulation results. This sequence reflects the entire process of groundwater level fluctuations over time at that spatial location.

[0113] Step S740: Based on the groundwater level change sequence, use time series constraints to perform constraint discrimination on each evaluation grid unit, and update the suitability level of the evaluation grid unit in the target evaluation model according to the discrimination results of each evaluation grid unit.

[0114] The groundwater level change sequence refers to the sequence of groundwater level elevation values ​​extracted from the unsteady flow simulation results for each evaluation grid cell and arranged in chronological order. The time series constraints are hard criteria set based on the groundwater level change sequence to determine whether the evaluation grid cell experiences an unacceptable continuous decline in water level or a risk of desiccation during long-term operation. These constraints may include at least one of the following: whether the cumulative decline in the groundwater level of the evaluation grid cell exceeds a preset threshold within a continuous preset time window; or whether the groundwater level of the evaluation grid cell is lower than a preset minimum allowable water level at any time during the simulation period.

[0115] In the continuous decline constraint, if the groundwater level of the evaluation grid unit shows a continuous downward trend within a preset time window and the cumulative decline exceeds a preset threshold, it is judged as dynamically unsuitable. In the minimum water level constraint, if the groundwater level of the evaluation grid unit is lower than the preset minimum allowable water level at any time during the simulation period, it is judged as dynamically unsuitable. Dynamic unsuitability refers to a set of evaluation grid units that, although passing the constraint judgment in the static suitability evaluation and even obtaining a high comprehensive suitability index, are judged as unsuitable by the time series constraint conditions due to the continuous decline or excessive fluctuation of the groundwater level under the influence of long-term climate fluctuations or mining activities.

[0116] Furthermore, the suitability level of the evaluation grid units in the target evaluation model can be updated based on the judgment results of each evaluation grid unit. That is, for grid units marked as dynamically unsuitable areas, their suitability level is forcibly updated to the level corresponding to unsuitability. For grid units that pass dynamic judgment, their original static suitability level remains unchanged.

[0117] Based on this, the updated target evaluation model is a dynamic target evaluation model. Its suitability level not only reflects the current static conditions, but also integrates the dynamic adaptability under the influence of long-term climate fluctuations and mining activities, which can provide a guarantee for the scientific nature and engineering safety of reservoir site selection.

[0118] In an exemplary embodiment of this disclosure, a device for evaluating the suitability of an underground reservoir is also provided. (See reference...) Figure 8 As shown, the underground reservoir suitability evaluation device 800 may include a first model construction module 810, a determination module 820, an indicator system construction module 830, a second model construction module 840, and an evaluation module 850. Specifically: The first model construction module 810 is used to construct a three-dimensional geological model of the area to be evaluated based on borehole stratification information, geological profile information, and geophysical data. The three-dimensional geological model includes the spatial distribution, thickness distribution, and boundary conditions of aquifers and impermeable layers. The determination module 820 is used to determine the three-dimensional distribution field of hydrogeological parameters of the area to be evaluated based on the hydrogeological parameters and the three-dimensional geological model. The index system construction module 830 is used to determine the evaluation index system corresponding to the area to be evaluated and to perform index quantification calculations on each evaluation grid unit in the three-dimensional geological model to obtain the quantified index of each evaluation grid unit. The second model construction module 8 40 is used to perform constraint condition discrimination on each evaluation grid unit based on the three-dimensional geological model and the three-dimensional distribution field of hydrogeological parameters, and to mask the three-dimensional geological model based on the discrimination results to obtain a reference geological model; Evaluation module 850 is used to determine the weight of the quantitative index of each evaluation grid unit in the reference geological model, and to determine the comprehensive suitability index of the evaluation grid unit based on the weight and the corresponding quantitative index, so as to classify the reference geological model based on the comprehensive suitability index and generate a target evaluation model. The target evaluation model is used to characterize the suitability of underground reservoir construction in the area to be evaluated.

[0119] Since the details of each functional module of the underground reservoir suitability evaluation device of the exemplary embodiments of this disclosure have been described in the exemplary embodiments of the underground reservoir suitability evaluation method described above, they will not be repeated here.

[0120] It should be noted that although several modules or units of the underground reservoir suitability assessment device have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0121] An exemplary embodiment of this disclosure also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the above-described method for assessing the suitability of underground reservoirs.

[0122] In one implementation, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing a computer program, such as read-only memory, NAND flash memory, etc.

[0123] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0124] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0125] Computer programs can be carried or transmitted via signals such as electricity, magnetism, light, electromagnetic radiation, and infrared radiation. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to execute by the processor of the electronic device) the method steps of various exemplary embodiments of this disclosure, such as the underground reservoir suitability evaluation method described above.

[0126] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform an exemplary method of any of the above-described methods by executing the executable instructions.

[0127] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which may be referred to as "circuit," "module," or "system," respectively.

[0128] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A method for evaluating the suitability of an underground reservoir, characterized in that, include: Based on borehole stratification information, geological profile information and geophysical data of the area to be evaluated, a three-dimensional geological model of the area to be evaluated is constructed. The three-dimensional geological model includes the spatial distribution, thickness distribution and boundary conditions of aquifers and impermeable layers. Based on the hydrogeological parameters of the area to be evaluated and the three-dimensional geological model, determine the three-dimensional distribution field of the hydrogeological parameters of the area to be evaluated; The evaluation index system corresponding to the area to be evaluated is determined, and the index quantification calculation is performed on each evaluation grid unit in the three-dimensional geological model to obtain the quantified index of each evaluation grid unit. For each evaluation grid cell, based on the three-dimensional geological model and the three-dimensional distribution field of the hydrogeological parameters, the evaluation grid cell is subjected to constraint condition discrimination based on preset constraints, and the three-dimensional geological model is masked according to the discrimination result to obtain a reference geological model; For each evaluation grid cell in the reference geological model, the weight of the quantitative index of the evaluation grid cell is determined, and the comprehensive suitability index of the evaluation grid cell is determined according to the weight and the corresponding quantitative index. The reference geological model is then graded based on the comprehensive suitability index to generate a target evaluation model. The target evaluation model is used to characterize the suitability of underground reservoir construction in the area to be evaluated.

2. The method according to claim 1, characterized in that, The process of constructing a three-dimensional geological model of the area to be evaluated based on borehole stratification information, geological profile information, and geophysical data includes: The borehole stratification information, the geological profile information, and the geophysical data information are standardized respectively to obtain standardized borehole stratification information, standardized geological profile information, and standardized geophysical data information. Based on the standardized borehole layering information, a three-dimensional trajectory line of the borehole is generated in three-dimensional space, and the layering points are calibrated along the three-dimensional trajectory line to obtain the layering points of the top plate and the bottom plate of the aquifer. A spatial interpolation algorithm is used to generate a three-dimensional surface of the top interface of the aquifer based on the layering points of the top plate of the aquifer, and a three-dimensional surface of the bottom interface of the aquifer based on the layering points of the bottom plate. The spatial distribution and thickness distribution of the aquifer and the impermeable layer are determined according to the spatial positional relationship between the three-dimensional surface of the top interface and the three-dimensional surface of the bottom interface. Using the standardized geological profile information and standardized geophysical data, spatial constraints and corrections are applied to the three-dimensional curved surfaces of the top and bottom interfaces. Lateral and vertical boundary conditions are defined for the three-dimensional spatial structure enclosed by the three-dimensional curved surfaces of the top and bottom interfaces, thus obtaining the boundary conditions. Based on the spatial distribution and thickness distribution of the aquifer and impermeable layer and the boundary conditions, the three-dimensional geological model is generated, and the three-dimensional geological model is divided into three-dimensional meshes to obtain discretized evaluation mesh units.

3. The method according to claim 1, characterized in that, The step of determining the three-dimensional distribution field of the hydrogeological parameters of the area to be evaluated based on the hydrogeological parameters of the area to be evaluated and the three-dimensional geological model includes: The hydrogeological parameters are subjected to quality inspection to determine the hydrogeological parameters that pass the inspection. Based on the three-dimensional geological model, values ​​are assigned to each evaluation grid cell in the three-dimensional geological model using the verified hydrogeological parameters; Based on preset lithological constraints, interpolation calculations are performed on each evaluated grid cell after assignment to generate the three-dimensional distribution field of the hydrogeological parameters.

4. The method according to claim 1, characterized in that, The preset constraints include at least engineering safety constraints, water resource development constraints, and ecological environment constraints; For each evaluation grid cell, based on the three-dimensional geological model and the three-dimensional distribution field of the hydrogeological parameters, the evaluation grid cell is subjected to constraint condition discrimination based on preset constraints. Then, based on the discrimination result, the three-dimensional geological model is masked to obtain a reference geological model, including: For the evaluation grid cell, determine whether the thickness of the waterproof layer meets the engineering safety constraints; Based on the three-dimensional distribution field of the hydrogeological parameters, determine whether the groundwater at the location of the evaluation grid unit meets the water resource development constraints; Based on the groundwater level elevation at different time points in the three-dimensional distribution field of the hydrogeological parameters, the groundwater level change range corresponding to the evaluation grid unit is determined to determine whether the groundwater level change range meets the ecological environment constraints. The evaluation grid cells that fail to meet at least one of the engineering safety constraints, the water resource development constraints, and the ecological environment constraints are masked to obtain the reference geological model.

5. The method according to claim 1, characterized in that, The evaluation index system includes water storage space condition index, hydrogeological condition index, recharge and hydrodynamic condition index, and engineering and ecological condition index. For each evaluation grid cell in the reference geological model, the weight of the quantitative index of the evaluation grid cell is determined, and the comprehensive suitability index of the evaluation grid cell is determined based on the weight and the corresponding quantitative index, including: A hierarchical model is constructed, which includes a target layer, a criterion layer, and an indicator layer. The target layer is used to characterize the comprehensive suitability of underground reservoir construction. The criterion layer includes water storage space conditions, hydrogeological conditions, recharge and hydrodynamic conditions, and engineering and ecological conditions. The indicator layer includes multiple evaluation indicators belonging to each criterion layer. Based on the hierarchical structure model, a judgment matrix corresponding to each layer is constructed, and the local weights of the evaluation indicators in each layer are calculated according to the judgment matrix, so as to obtain the global weight of the corresponding evaluation indicator by weighting the local weights. The reference geological model is traversed to filter out effective evaluation grid cells that are not masked. For each effective evaluation grid cell, the comprehensive suitability index of the effective evaluation grid cell is obtained by weighted summation based on the global weight and the quantitative index of the effective evaluation grid cell.

6. The method according to claim 5, characterized in that, Before calculating the local weights of the evaluation indicators in each layer and then weighting these local weights to obtain the global weights of each evaluation indicator, the method further includes: For each of the judgment matrices, a consistency check is performed, and the target matrix elements in the judgment matrices that fail the check are adjusted until the judgment matrix passes the consistency check.

7. The method according to claim 1, characterized in that, The step of classifying and determining the reference geological model based on the comprehensive suitability index to generate a target evaluation model includes: Traverse all evaluation grid cells in the reference geological model. If the current evaluation grid cell has been masked, it is determined to be of an unsuitable level. If the current evaluation grid cell is not masked, the suitability level of the current evaluation grid cell is determined according to the comprehensive suitability index corresponding to the current evaluation grid cell and the preset suitability grading rules. The determined suitability levels are added as grading attributes to the evaluation grid cells corresponding to the reference geological model to obtain the target evaluation model.

8. The method according to claim 1, characterized in that, After classifying and determining the reference geological model based on the comprehensive suitability index and generating the target evaluation model, the method further includes: Obtain a dataset of replenishment conditions for multiple scenarios, where the multiple scenarios include at least a high water level scenario, a normal water level scenario, and a low water level scenario; Using the target evaluation model as the spatial framework and the three-dimensional distribution field of the hydrogeological parameters as the parameter input, a numerical simulation model for unsteady flow groundwater is constructed. The recharge condition dataset is used as a boundary condition input into the unsteady flow groundwater numerical simulation model to perform long-term unsteady flow simulations under various scenarios, thereby obtaining the groundwater level change sequence of each evaluation grid cell of the target evaluation model under different simulation time steps. Based on the groundwater level change sequence, time series constraints are used to perform constraint discrimination on each evaluation grid unit, and the suitability level of the evaluation grid unit in the target evaluation model is updated according to the discrimination results of each evaluation grid unit. The time series constraints include at least one of the following: whether the cumulative decrease in the groundwater level of the evaluation grid cell exceeds a preset threshold within a continuous preset time window, and whether the groundwater level of the evaluation grid cell is lower than a preset minimum allowable water level at any time during the simulation period.

9. The method according to claim 5, characterized in that, The water storage space condition indicators include at least the effective thickness of the aquifer and the effective water storage volume; the hydrogeological condition indicators include at least the permeability coefficient and the groundwater level depth; the recharge and hydrodynamic condition indicators include at least the natural recharge intensity and the groundwater runoff intensity; and the engineering and ecological condition indicators include at least the aquitard integrity index.

10. A device for evaluating the suitability of an underground reservoir, characterized in that, The device includes: The first model construction module is used to construct a three-dimensional geological model of the area to be evaluated based on borehole stratification information, geological profile information and geophysical data information. The three-dimensional geological model includes the spatial distribution, thickness distribution and boundary conditions of aquifers and impermeable layers. The determination module is used to determine the three-dimensional distribution field of the hydrogeological parameters of the area to be evaluated based on the hydrogeological parameters of the area to be evaluated and the three-dimensional geological model. The indicator system construction module is used to determine the evaluation indicator system corresponding to the area to be evaluated, and to perform indicator quantification calculation on each evaluation grid unit in the three-dimensional geological model to obtain the quantified indicators of each evaluation grid unit. The second model construction module is used to perform constraint condition discrimination on each evaluation grid cell based on the three-dimensional geological model and the three-dimensional distribution field of the hydrogeological parameters, and to perform masking on the three-dimensional geological model based on the discrimination result to obtain a reference geological model. The evaluation module is used to determine the weight of the quantitative index of each evaluation grid cell in the reference geological model, and to determine the comprehensive suitability index of the evaluation grid cell based on the weight and the corresponding quantitative index, so as to classify the reference geological model based on the comprehensive suitability index and generate a target evaluation model. The target evaluation model is used to characterize the suitability of underground reservoir construction in the area to be evaluated.

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