A dynamic construction method of a geological three-dimensional model based on digital twinning
Patent Information
- Application Number
- CN202610946275.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-29
- Publication Date
- 2026-09-22
AI Technical Summary
[0005]本发明提供一种基于数字孪生的地质三维模型动态构建方法，以解决传统的地质三维模型构建方法未充分考虑断层等地质结构对空间连续性的影响、缺乏多因素综合分析机制，且缺乏有效的约束调节机制的技术问题
[0021]1、本发明提出一种断层屏蔽约束机制，能够将断层对地质体连续性的切断作用显式引入距离度量过程，减少跨断层样本误参与造成的地质属性平滑扩散现象，提高断裂发育区域属性场分布结果与实际地质结构的一致性；同时，未改变普通Kriging求解主框架，便于直接部署到现有地学建模程序中，实现成本低、可解释性强。
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Figure CN122799017A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of computer data processing and geological 3D modeling technology, and in particular to a method for dynamically constructing geological 3D models based on digital twins. Background Technology
[0002] With the increasing demands for geological engineering, underground space development, and geological disaster early warning, 3D geological models have become an important technical means to describe the underground geological structure and physical properties. Traditional geological modeling methods are usually based on discrete geological information such as borehole data and fault interpretation data, constructing the distribution of underground geological properties through spatial interpolation methods, and further forming a 3D geological model. However, these methods are mostly used for one-time modeling, and the resulting models are usually static models, which are difficult to reflect the dynamic evolution process of geological bodies under engineering disturbances or changes in natural conditions.
[0003] With the development of digital twin technology, the dynamic updating of geological three-dimensional models by introducing monitoring data has gradually become a research hotspot. However, the following main problems still exist in practical applications: (1) The influence of geological structures such as faults on spatial continuity is not fully considered in the geological modeling process. Existing interpolation methods assume that spatial continuity is generally valid and do not fully consider the destructive effect of geological structures such as faults on spatial continuity. It is easy to incorrectly reference sampling points on both sides of the fault, resulting in the smooth diffusion of cross-fault attributes and reducing the consistency between the model and the actual geological structure. (2) There is a lack of multi-factor comprehensive analysis mechanism in the model update judgment process. The update area is identified based on a single change index (such as attribute difference or change rate). The local spatial change characteristics and geological structure influence are not comprehensively considered, resulting in insufficient ability to identify changes in complex areas and difficulty in accurately depicting key change areas. (3) There is a lack of effective constraint adjustment mechanism in the model update method. Usually, the change amount is directly superimposed on the original model attributes. The degree of change in different areas is not differentiated and controlled, which can easily cause local attribute mutations or numerical oscillations, thereby affecting the stability and continuity of model updates.
[0004] Therefore, in order to address the above shortcomings, there is an urgent need to provide a method for dynamically constructing geological 3D models based on digital twins. Summary of the Invention
[0005] This invention provides a dynamic construction method for geological 3D models based on digital twins, which solves the technical problems of traditional geological 3D model construction methods that do not fully consider the impact of geological structures such as faults on spatial continuity, lack a multi-factor comprehensive analysis mechanism, and lack an effective constraint and adjustment mechanism.
[0006] The present invention provides a method for dynamically constructing a geological three-dimensional model based on digital twins, comprising the following steps:
[0007] S1. Collect basic geological data and monitoring data within the current update cycle of the target geological area, and preprocess them respectively to obtain preprocessed basic geological data and preprocessed monitoring data; divide the target geological area into voxel units, introduce a fault shielding constraint mechanism, and calculate the improved equivalent distance; based on the improved equivalent distance, obtain the geological attribute benchmark values of the voxel units, and construct the initial three-dimensional geological model of the target geological area;
[0008] S2. Based on the preprocessed monitoring data, calculate the monitoring attribute values of voxel units and obtain the geological attribute changes of voxel units; based on the geological attribute changes of voxel units and combined with the geological attribute benchmark values of corresponding voxel units, screen voxel units to obtain candidate updated voxel units; based on the candidate updated voxel units, construct a voxel multi-factor comprehensive evaluation model and calculate the classification judgment value of the candidate updated voxel units; based on the classification judgment value of the candidate updated voxel units, constrain and control the geological attribute changes of voxel units to obtain the updated geological attribute values of the candidate updated voxel units, and replace the geological attribute benchmark values of the corresponding voxel units in the initial geological 3D model.
[0009] Preferably, the basic geological data includes borehole data and fault interpretation data; the borehole data and fault interpretation data are preprocessed to obtain preprocessed borehole data and preprocessed fault interpretation data, respectively; and a fault constraint surface model is constructed based on the preprocessed fault interpretation data.
[0010] Preferably, the specific implementation process of the fault shielding constraint mechanism includes:
[0011] Based on the Euclidean distance between the candidate sampling point and the point to be estimated, combined with the fault penalty term, an improved equivalent distance is calculated. Based on the improved equivalent distance, the geological attribute estimate of the point to be estimated in the voxel element is calculated and assigned to the corresponding voxel element as the geological attribute benchmark value of the corresponding voxel element. The candidate sampling point is selected by local search neighborhood with the center point of the voxel element as the point to be estimated. Based on the fault constraint surface model, the fault isolation discrimination factor between the candidate sampling point and the point to be estimated is generated, and the fault penalty term is constructed.
[0012] Preferably, the specific screening process for the candidate updated voxel units is as follows:
[0013] Based on the preprocessed monitoring data, the periodic representative value of the monitoring point is calculated; based on the periodic representative value of the monitoring point whose coordinates fall within the spatial range of the voxel unit, the monitoring attribute value of the voxel unit is obtained; the monitoring attribute value of the voxel unit is converted into the geological attribute change of the voxel unit through a preset physical correlation, and combined with the geological attribute benchmark value of the voxel unit, the relative change rate of the geological attribute is calculated; based on the relative change rate of the geological attribute, the changing units and stable voxel units are marked, and the changing units are used as candidate updated voxel units.
[0014] Preferably, the voxel multi-factor comprehensive evaluation model is constructed based on the relative change rate of geological attributes, the intensity of local structural changes, and the fault proximity.
[0015] Preferably, the specific generation process of the local structural change intensity and fault proximity includes:
[0016] The intensity of local structural changes is quantified by calculating the average of the absolute values of the differences in geological property changes between a candidate updated voxel and all its adjacent candidate updated voxel; fault proximity is constructed based on the minimum Euclidean distance from the center of the candidate updated voxel to each fault in the fault constraint surface model.
[0017] Preferably, the specific calculation process for the updated geological attribute values of the candidate updated voxel units is as follows:
[0018] Based on the hierarchical judgment value of the candidate updated voxel unit, the attribute update adjustment coefficient of the candidate updated voxel unit is generated to constrain and control the change of geological attributes of the candidate updated voxel unit. Combined with the geological attribute benchmark value of the candidate updated voxel unit, the updated geological attribute value of the candidate updated voxel unit is calculated.
[0019] Preferably, the geological attribute baseline values of the corresponding voxel units in the initial geological 3D model of the target geological area are replaced with the updated geological attribute values of the candidate updated voxel units to update the local attributes; monitoring data is continuously introduced and the above update process is repeated in different update cycles to dynamically construct the geological 3D model.
[0020] The beneficial effects of the technical solution of the present invention are:
[0021] 1. This invention proposes a fault shielding constraint mechanism, which can explicitly introduce the cutting effect of faults on the continuity of geological bodies into the distance measurement process, reduce the smooth diffusion phenomenon of geological attributes caused by the erroneous participation of cross-fault samples, and improve the consistency between the attribute field distribution results in the fault development area and the actual geological structure; at the same time, it does not change the general framework of the ordinary Kriging solution, which is easy to directly deploy into existing geoscience modeling programs, achieving low cost and strong interpretability.
[0022] 2. This invention proposes a voxel multi-factor comprehensive evaluation model based on the relative change rate of geological attributes, the intensity of local structural changes, and fault proximity. This model achieves an integrated characterization of the change intensity, complexity, and sensitivity of candidate updated voxel units, which can improve the precision of change identification and the consistency of spatial response, and provide a highly reliable basis for the stable dynamic updating of geological 3D models.
[0023] 3. This invention proposes a constraint-based attribute update mechanism based on hierarchical judgment values. By introducing the hierarchical judgment values of candidate update voxel units into the attribute update process, it achieves adaptive adjustment of geological attribute changes. This allows areas with significant changes to fully reflect geological attribute changes driven by monitoring data, while areas with weaker changes maintain a smooth transition. This effectively avoids attribute abrupt changes and numerical oscillations caused by traditional direct superposition update methods. At the same time, it eliminates the need for complex models or additional calculation processes, improving the reliability and engineering applicability of dynamic updates of the geological 3D model while ensuring update stability. Attached Figure Description
[0024] Figure 1 This is a flowchart of a method for dynamically constructing a geological three-dimensional model based on digital twins, as described in this invention. Detailed Implementation
[0025] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0027] The following description, in conjunction with the accompanying drawings, details a specific scheme for a dynamic construction method of a geological three-dimensional model based on digital twins provided by this invention.
[0028] See attached document Figure 1 The diagram illustrates a flowchart of a method for dynamically constructing a geological 3D model based on digital twins, according to an embodiment of the present invention. The method includes the following steps:
[0029] S1. Collect basic geological data and monitoring data within the current update cycle of the target geological area, and preprocess them respectively to obtain preprocessed basic geological data and preprocessed monitoring data; divide the target geological area into voxel units, introduce a fault shielding constraint mechanism, and calculate the improved equivalent distance; based on the improved equivalent distance, obtain the geological attribute benchmark values of the voxel units, and construct the initial three-dimensional geological model of the target geological area;
[0030] The system collects basic geological data and monitoring data within the current update cycle for the target geological area. The basic geological data includes borehole data and fault interpretation data. Borehole data is obtained from borehole logging data, and fault interpretation data is obtained from geological mapping data. The monitoring data includes groundwater level monitoring data or pore water pressure data. Groundwater level monitoring data is obtained from water level gauges at monitoring points, and pore water pressure data is obtained from pore pressure gauges at monitoring points. In a specific embodiment, the monitoring data here is groundwater level monitoring data. The monitoring points are spatial locations where monitoring equipment is deployed to obtain dynamic monitoring data. The update cycle is the time length corresponding to one geological 3D model update performed by the digital twin system, such as 10 minutes.
[0031] The aforementioned basic geological data and monitoring data were preprocessed to obtain preprocessed basic geological data and preprocessed monitoring data. For borehole data, a seven-parameter spatial coordinate transformation model was used to transform the borehole data to a unified reference coordinate system, resulting in borehole data in the unified coordinate system. An outlier detection method based on absolute median difference was then used to remove outliers from the borehole data in the unified coordinate system, resulting in preprocessed borehole data. For fault interpretation data, a seven-parameter spatial coordinate transformation model was used to transform the fault interpretation data to the same unified coordinate system as the borehole data, resulting in preprocessed fault interpretation data. Fault points or fault lines in the preprocessed fault interpretation data were then used to generate fault constraint surface models using an irregular triangular mesh construction method to describe the distribution and geometry of faults in three-dimensional space. The preprocessed basic geological data includes preprocessed borehole data and preprocessed fault interpretation data. For the monitoring data, a seven-parameter spatial coordinate transformation model is used to transform the monitoring data to the same coordinate system as the borehole data, resulting in monitoring data in the unified coordinate system. Then, an outlier detection method based on absolute median difference is used to remove outliers from the monitoring data in the unified coordinate system, resulting in outlier-removed monitoring data. A sliding window averaging method is used to smooth the outlier-removed monitoring data, resulting in preprocessed monitoring data. The sliding window size is set according to the sampling time interval of the monitoring data, preferably 3 to 10 times the sampling time interval. The above basic geological data, monitoring data, preprocessed basic geological data, and preprocessed monitoring data are stored in a geological database.
[0032] The first part of the preprocessed borehole data The spatial coordinates of each sampling point are: ,in Indicates the first The x-coordinate of each sampling point Indicates the first The ordinate of each sampling point Indicates the first The elevation coordinates of each sampling point; the sampling points in the borehole data are spatial location points where geological attribute observations were obtained at the borehole location; the first The geological attribute observation values at each sampling point are denoted as follows: The observed geological attributes are stored in a geological database. The observed geological attributes are porosity or water content values, directly taken from the corresponding attribute fields in the pre-processed borehole data. In a specific embodiment, the observed geological attributes are set to water content. The target geological area is divided into sections according to a preset spatial resolution (e.g., 5m × 5m). Individual voxel units, with the center point of each voxel unit as the point to be estimated; for the th voxel unit... The point to be estimated for an individual element is represented by the following spatial coordinates: Centered on the spatial coordinates, a local search neighborhood is established using a spherical search method based on the interpolation search radius to select candidate sampling points for interpolation calculation. The interpolation search radius is taken as 1 to 1.5 times the average borehole spacing within the target geological area, and the average borehole spacing is calculated by averaging the Euclidean distances between the spatial coordinates of each borehole sampling point within the target geological area. The sampling point and the first When the Euclidean distance between the points to be estimated in an individual element is less than or equal to the interpolation search radius, the first element will be... 1 sampling point as candidate sampling point ;
[0033] To overcome the problem of distorted attribute estimation caused by the traditional Kriging interpolation method in fault-developed regions due to the assumption that spatial continuity is generally true and the tendency to incorrectly reference sampling points across faults, a fault shielding constraint mechanism is proposed. Based on the calculation of the Euclidean distance between candidate sampling points and the point to be estimated, a fault penalty term is introduced by determining whether a fault barrier exists between the candidate sampling point and the point to be estimated, thereby calculating the improved equivalent distance, as shown in the following formula: , in, Indicates the first The candidate sampling point and the first Improved equivalent distance between the estimated points of individual units; Indicates the first The candidate sampling point and the first The Euclidean distance between the points to be estimated in the individual unit; For the first Spatial coordinates of the candidate sampling points; For the first Spatial coordinates of the point to be estimated in the individual element unit; Indicates the first The candidate sampling point and the first The fault barrier discrimination factor between the points to be estimated in the individual element unit indicates the presence of fault barrier when the spatial line connecting the candidate sampling point and the point to be estimated intersects with the aforementioned fault constraint surface model. The value is 1; otherwise, it indicates that there is no fault barrier. The value is 0; This represents the fault shielding penalty distance, which is 1.5 to 3 times the interpolation search radius, preferably 2 times. Indicates the fault penalty item;
[0034] The above formula proposes a fault shielding constraint mechanism in the ordinary Kriging interpolation method that only calculates Euclidean distance. This mechanism can explicitly introduce the cutting effect of faults on the continuity of geological bodies into the distance measurement process, reduce the smooth diffusion phenomenon of geological attributes caused by the erroneous participation of cross-fault samples, and improve the consistency between the attribute field distribution results in the fault-developed area and the actual geological structure. At the same time, it does not change the main framework of the ordinary Kriging solution, making it easy to directly deploy into existing geoscientific modeling programs, achieving low cost and strong interpretability.
[0035] Replacing the Euclidean distance in the ordinary Kriging interpolation method with the improved equivalent distance allows for the calculation of the first... Geological attribute estimates of the points to be estimated for individual element units Repeat the above process for all points to be estimated within the target geological area to obtain the estimated geological attribute values for each point. Assign these values to the corresponding voxel units as the benchmark values for the geological attributes of those voxel units. Furthermore, an initial three-dimensional geological model of the target geological region is constructed based on the spatial coordinates of each voxel unit and the corresponding geological attribute benchmark values.
[0036] S2. Based on the preprocessed monitoring data, calculate the monitoring attribute values of voxel units and obtain the geological attribute changes of voxel units; based on the geological attribute changes of voxel units and combined with the geological attribute benchmark values of corresponding voxel units, screen voxel units to obtain candidate updated voxel units; based on the candidate updated voxel units, construct a voxel multi-factor comprehensive evaluation model and calculate the classification judgment value of the candidate updated voxel units; based on the classification judgment value of the candidate updated voxel units, constrain and control the geological attribute changes of voxel units to obtain the updated geological attribute values of the candidate updated voxel units, and replace the geological attribute benchmark values of the corresponding voxel units in the initial geological 3D model.
[0037] For monitoring points For all updates within the current update cycle The average value of each preprocessed monitoring data point is calculated to obtain the monitoring point. The periodic value represents the value. =Update cycle duration / Sampling time interval; Monitoring points Spatial coordinates are When the coordinates of the monitoring point fall into the first Within the spatial range of a voxel unit, the periodic representative value of the monitoring point is assigned to that voxel unit. If multiple monitoring points exist within the voxel unit, the arithmetic mean of the periodic representative values of all monitoring points is taken as the monitoring attribute value of the voxel unit. The monitoring attribute value of the voxel unit is converted into the geological attribute change of the voxel unit through a preset physical correlation. The physical correlation is established based on the physical response mechanism between the monitoring data and the geological attribute observation values, and is quantitatively expressed using a function mapping. For example, for groundwater level monitoring data, the groundwater level change can be mapped to the water content change, and its expression is: ,in, Indicates the first Changes in water content of individual units (changes in geological properties). Indicates the first Groundwater level value within the current update cycle of an individual element (preprocessed monitoring data); The reference water level value is obtained by calculating the statistical average of historical monitoring data. The historical monitoring data is taken from an existing geological database, preferably pre-processed monitoring data from 5 to 20 consecutive update cycles prior to the current update cycle. The mapping coefficient is used to characterize the degree of influence of groundwater level changes on geological property changes. It is determined by regression analysis between historical monitoring data and corresponding geological property observation data, and the least squares method is preferred for linear regression solution.
[0038] Furthermore, based on the first Geological attribute variation of individual element units Geological property benchmark values of corresponding voxel units Calculate the relative rate of change of geological properties for voxel units, when the relative rate of change of geological properties... When the change exceeds a preset threshold, the voxel unit is marked as a changed unit and used as a candidate updated voxel unit; otherwise, it is marked as a stable voxel unit. The change threshold is preferably set at the 80th to 90th percentile of the relative change rate of geological attributes in the target geological region, and more preferably at the 85th percentile, in order to screen out candidate updated voxel units with significant changes. For stable voxel units, their geological attribute baseline values are kept unchanged and they are not included in the subsequent geological attribute update process. For candidate updated voxel units, they are included in the subsequent geological attribute update process.
[0039] To overcome the problems of existing geological 3D model updating methods, which fail to balance the fine representation of key areas with updating efficiency and the unreasonable allocation of computational resources due to the uniform voxel update granularity applied to different change regions, this paper proposes a multi-factor comprehensive evaluation model for voxels based on the relative change rate of geological attributes, the intensity of local structural changes, and fault proximity. The model calculates the classification value of each candidate updated voxel unit by summing the multi-factor indices of each candidate updated voxel unit, as shown in the following formula: , in, Indicates the first The hierarchical determination value of each candidate updated voxel unit; Indicates the first The relative rate of change of geological attributes of each candidate updated voxel unit is used to reflect the significance of the change in geological attributes. Indicates the first Changes in geological properties of candidate updated voxel units; Indicates the first Geological attribute baseline values for each candidate updated voxel unit; Indicates the first The local structural complexity within the neighborhood of each candidate updated voxel unit; Indicates the first The intensity of local structural change within the neighborhood of the candidate updated voxel unit is calculated as follows: [The text abruptly ends here, so the translation stops.] The absolute value of the difference in geological property changes between a candidate updated voxel and all its adjacent candidate updated voxel, and the average of the absolute values. The normalized baseline quantity representing the intensity of local structural changes is the 95th percentile of the intensity of local structural changes in all candidate updated voxel units within the target geological region. Indicates the first Fault proximity of candidate updated voxel units, when When the fault proximity is 0, At that time, the fault proximity was ; Indicates the first The minimum Euclidean distance from the center of each candidate updated voxel unit to each fault plane in the fault constraint surface model; The reference distance representing the fault's influence range is 1 to 2 times the interpolation search radius;
[0040] The above formula proposes a voxel multi-factor comprehensive evaluation model based on the relative change rate of geological attributes, the intensity of local structural changes, and the proximity of faults. This model achieves an integrated characterization of the change intensity, complexity, and sensitivity of candidate updated voxel units, which can improve the precision of change identification and the consistency of spatial response, and provide a highly reliable basis for the stable dynamic updating of geological 3D models.
[0041] To overcome the problems of abrupt attribute changes and unstable update results caused by directly superimposing geological attribute changes during existing geological 3D model updates, a constraint-based attribute update mechanism based on hierarchical judgment values is proposed. Based on the hierarchical judgment values of candidate update voxel units, attribute update adjustment coefficients are generated, and the geological attribute changes are constrained and controlled. The updated geological attribute values for each candidate update voxel unit are then calculated, as shown in the following formula: , in, Indicates the first The updated geological attribute values of each candidate updated voxel unit; Indicates the first Geological attribute baseline values for each candidate updated voxel unit; Indicates the first The attribute update adjustment coefficients for each candidate updated voxel unit are determined by taking values of 1 and a grading decision value. The minimum value in the range is used to control the degree of influence of changes in geological properties; Indicates the first The hierarchical determination value of each candidate updated voxel unit; Indicates the first Changes in geological properties of candidate updated voxel units;
[0042] The above formula proposes a constraint-based attribute update mechanism based on hierarchical judgment values. By introducing the hierarchical judgment values of candidate update voxel units into the attribute update process, it achieves adaptive adjustment of geological attribute changes. This allows areas with significant changes to fully reflect geological attribute changes driven by monitoring data, while areas with weak changes maintain a smooth transition. This effectively avoids attribute abrupt changes and numerical oscillations caused by traditional direct superposition update methods. At the same time, it eliminates the need for complex models or additional calculation processes, improving the reliability and engineering applicability of dynamic updates of the geological 3D model while ensuring update stability.
[0043] The updated geological attribute values of candidate updated voxel units replace the geological attribute baseline values of the corresponding voxel units in the initial geological 3D model of the target geological area, thereby achieving accurate updates of local attributes. Monitoring data is continuously introduced and the above update process is repeated in different update cycles, thereby realizing the dynamic construction of a geological 3D model based on digital twins.
[0044] In summary, a method for dynamically constructing geological 3D models based on digital twins has been developed.
[0045] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.
[0046] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0047] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.
Claims
1. A method for dynamically constructing a geological 3D model based on digital twins, characterized in that, Includes the following steps: S1. Collect basic geological data and monitoring data within the current update cycle for the target geological area, and preprocess them respectively to obtain preprocessed basic geological data and preprocessed monitoring data; The target geological region is divided into voxel units, and a fault shielding constraint mechanism is introduced to calculate the improved equivalent distance. Based on the improved equivalent distance, the geological attribute benchmark values of the voxel units are obtained, and an initial three-dimensional geological model of the target geological region is constructed. S2. Based on the preprocessed monitoring data, calculate the monitoring attribute values of the voxel units and obtain the geological attribute changes of the voxel units; based on the geological attribute changes of the voxel units and combined with the geological attribute benchmark values of the corresponding voxel units, screen the voxel units to obtain candidate updated voxel units. Based on candidate updated voxel units, a multi-factor comprehensive evaluation model for voxels is constructed, and the classification judgment value of candidate updated voxel units is calculated. Based on the hierarchical judgment value of the candidate updated voxel unit, the change in geological attributes of the voxel unit is constrained and controlled to obtain the updated geological attribute value of the candidate updated voxel unit, and the geological attribute benchmark value of the corresponding voxel unit in the initial geological 3D model is replaced.
2. The method for dynamically constructing a geological three-dimensional model based on digital twins according to claim 1, characterized in that, The basic geological data includes borehole data and fault interpretation data; the borehole data and fault interpretation data are preprocessed to obtain preprocessed borehole data and preprocessed fault interpretation data, respectively. Based on the preprocessed fault interpretation data, a fault constraint surface model is constructed.
3. The method for dynamically constructing a geological three-dimensional model based on digital twins according to claim 2, characterized in that, The specific implementation process of the fault shielding constraint mechanism includes: Based on the Euclidean distance between the candidate sampling point and the point to be estimated, combined with the fault penalty term, an improved equivalent distance is calculated. Based on the improved equivalent distance, the geological attribute estimate of the point to be estimated in the voxel element is calculated and assigned to the corresponding voxel element as the geological attribute benchmark value of the corresponding voxel element. The candidate sampling point is selected by local search neighborhood with the center point of the voxel element as the point to be estimated. Based on the fault constraint surface model, the fault isolation discrimination factor between the candidate sampling point and the point to be estimated is generated, and the fault penalty term is constructed.
4. The method for dynamically constructing a geological three-dimensional model based on digital twins according to claim 3, characterized in that, The specific screening process for the candidate updated voxel units is as follows: Based on the preprocessed monitoring data, the periodic representative value of the monitoring point is calculated; based on the periodic representative value of the monitoring point whose coordinates fall within the spatial range of the voxel unit, the monitoring attribute value of the voxel unit is obtained; the monitoring attribute value of the voxel unit is converted into the geological attribute change of the voxel unit through a preset physical correlation, and combined with the geological attribute benchmark value of the voxel unit, the relative change rate of the geological attribute is calculated; based on the relative change rate of the geological attribute, the changing units and stable voxel units are marked, and the changing units are used as candidate updated voxel units.
5. The method for dynamically constructing a geological three-dimensional model based on digital twins according to claim 4, characterized in that, The voxel multi-factor comprehensive evaluation model is constructed based on the relative change rate of geological attributes, the intensity of local structural changes, and the proximity of faults.
6. The method for dynamically constructing a geological three-dimensional model based on digital twins according to claim 5, characterized in that, The specific generation process of the local structural change intensity and fault proximity includes: The intensity of local structural changes is quantified by calculating the average of the absolute values of the differences in geological property changes between a candidate updated voxel and all its adjacent candidate updated voxel; fault proximity is constructed based on the minimum Euclidean distance from the center of the candidate updated voxel to each fault in the fault constraint surface model.
7. The method for dynamically constructing a geological three-dimensional model based on digital twins according to claim 1, characterized in that, The specific calculation process for the updated geological attribute values of the candidate updated voxel units is as follows: Based on the hierarchical judgment value of the candidate updated voxel unit, the attribute update adjustment coefficient of the candidate updated voxel unit is generated to constrain and control the change of geological attributes of the candidate updated voxel unit. Combined with the geological attribute benchmark value of the candidate updated voxel unit, the updated geological attribute value of the candidate updated voxel unit is calculated.
8. The method for dynamically constructing a geological three-dimensional model based on digital twins according to claim 7, characterized in that, The geological attribute baseline values of the corresponding voxel units in the initial geological 3D model of the target geological region are replaced with the updated geological attribute values of the candidate updated voxel units to update the local attributes; monitoring data is continuously introduced and the above update process is repeated in different update cycles to dynamically construct the geological 3D model.