A method and system for constructing a three-dimensional model of integrated well logging engineering

Abstract

This invention relates to the field of geological engineering technology, and in particular to a method and system for constructing a three-dimensional model of a comprehensive logging project. The method includes the following steps: acquiring remote sensing data for comprehensive logging project monitoring, and performing geological structure analysis based on the remote sensing data to obtain geological structure data; extracting multispectral features from the remote sensing data to obtain multispectral data; identifying surface lithology based on the multispectral data to obtain surface lithology data; and constructing a three-dimensional model of the comprehensive logging project based on the geological structure data and the surface lithology data. This invention constructs a dynamically updated and accurate three-dimensional model based on geological engineering technology, providing comprehensive support for the development and management of oil and gas fields.

Description

Technical Field

[0001] This invention relates to the field of geological engineering technology, and in particular to a method and system for constructing a three-dimensional model of a comprehensive logging project. Background Technology

[0002] Traditional oil and gas exploration technologies primarily rely on two-dimensional geological exploration and drilling data. However, with the increasing complexity of reservoirs, single two-dimensional data can no longer fully describe the spatial characteristics and properties of oil and gas reservoirs. Traditional three-dimensional models, once established, typically lack the ability to be updated in real time. During oil and gas extraction, reservoir properties change over time, with fluctuations in parameters such as temperature and pressure leading to variations in reservoir properties. Traditional three-dimensional modeling methods have poor adaptability to different geological environments and reservoir types. Due to the complex and variable geological environment, traditional methods often require customized modeling for different oil and gas fields or reservoir types, increasing the complexity and cost of system applications. Furthermore, for certain special geological conditions (such as complex faults and fractures), the modeling accuracy and performance of traditional methods are insufficient. Summary of the Invention

[0003] Therefore, the present invention needs to provide a method and system for constructing a three-dimensional model of a comprehensive logging project to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for constructing a three-dimensional model of a comprehensive logging project includes the following steps:

[0005] Step S1: Obtain remote sensing data for integrated well logging engineering monitoring, and perform geological structure analysis based on the integrated well logging engineering monitoring remote sensing data to obtain geological structure data;

[0006] Step S2: Extract multispectral features from the integrated well logging project monitoring remote sensing data to obtain multispectral data of the integrated well logging project; identify surface lithology based on the multispectral data of the integrated well logging project to obtain surface lithology data.

[0007] Step S3: Construct a three-dimensional model of the integrated logging project based on geological structure data and surface lithology data to obtain the three-dimensional model of the integrated logging project; perform spatial distribution analysis of oil and gas reservoirs based on the three-dimensional model of the integrated logging project to obtain spatial distribution data of oil and gas reservoirs.

[0008] Step S4: Acquire oil and gas reservoir temperature sensing data, and perform reservoir deformation analysis on the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature sensing data to obtain reservoir deformation data; perform reservoir rock stability analysis based on the reservoir deformation data to obtain reservoir rock stability data.

[0009] Step S5: Design the drilling path based on the reservoir rock stability data to obtain drilling path data; optimize the path of the integrated logging engineering 3D model based on the drilling path data to obtain the integrated logging engineering 3D optimized model, and upload it to the integrated logging engineering management platform to execute the model optimization task.

[0010] This invention, by acquiring remote sensing data and performing geological structure analysis, provides a comprehensive understanding of subsurface geological characteristics, helps identify potential oil and gas reservoir distributions, and lays the foundation for subsequent 3D model construction. Multispectral feature extraction and surface lithology identification effectively identify different lithological characteristics, enabling more accurate lithological classification based on geological structure data, providing more reliable input data, and further improving model accuracy. The construction of the 3D model combines geological structure and lithological data, allowing the model to more comprehensively represent the spatial distribution and characteristics of reservoirs, facilitating more refined oil and gas reservoir spatial distribution analysis in subsequent analyses, thereby ensuring the rational assessment and development of oil and gas resources. During real-time monitoring and analysis of oil and gas reservoirs, the introduction of temperature sensing data and reservoir deformation analysis allows for precise tracking of dynamic changes in the reservoir during extraction, timely detection of potential risks and problems, and early adjustments to avoid reservoir damage or accidents. Reservoir stability analysis, through further processing of deformation data, helps assess reservoir stability, guides safe extraction and drilling path optimization, and avoids drilling accidents caused by reservoir instability. By combining reservoir stability data and drilling path design, the drilling path is further optimized, which not only improves the safety of drilling operations but also effectively reduces costs. Overall, this method overcomes the problem of traditional models being unable to adapt to reservoir changes by enabling real-time updates and optimization of the 3D model. It effectively improves the extraction efficiency of oil and gas fields, reduces risks, and provides accurate and real-time data support and decision-making basis for oil and gas exploration and development.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Obtain the integrated logging engineering monitoring remote sensing data, and extract lidar features based on the integrated logging engineering monitoring remote sensing data to obtain lidar data;

[0013] Step S12: Perform fault structure identification on the lidar data to obtain fault structure data;

[0014] Step S13: Perform wrinkle structure identification on the lidar data to obtain wrinkle structure data;

[0015] Step S14: Merge the geological structures based on the fault structure data and fold structure data to obtain the geological structure data.

[0016] This invention significantly improves the accuracy and efficiency of geological feature identification during oil and gas exploration by integrating lidar technology. The acquisition and feature extraction of lidar data provide high-precision, high-resolution spatial information for subsequent geological analysis, facilitating more accurate acquisition of subsurface geological features. By identifying fault and fold structures in lidar data, complex structural features in oil and gas reservoirs, such as faults and folds, can be effectively identified. These are key factors affecting oil and gas distribution and reservoir stability. The identified fault and fold structure data help explorers better understand the spatial distribution and structural characteristics of oil and gas reservoirs, providing important evidence for accurate assessment and development of oil and gas resources. Further geological structure merging integrates fault and fold structure data to form more comprehensive geological structure data, providing high-quality, real-time updated geological information for 3D modeling in integrated logging engineering, avoiding the problem of traditional methods failing to reflect reservoir changes in a timely manner. This method allows for continuous optimization of 3D models in dynamically changing geological environments, improving the exploration accuracy and extraction efficiency of oil and gas reservoirs, while reducing risks caused by geological complexity, ensuring the safety and sustainability of oil and gas field development.

[0017] Optionally, step S12 specifically includes:

[0018] Step S121: Perform point cloud high reflectivity filtering on the lidar data to obtain point cloud filtered data;

[0019] Step S122: Generate a digital elevation model based on the filtered point cloud data to obtain the digital elevation model;

[0020] Step S123: Calculate the surface slope based on the digital elevation model to obtain surface slope data;

[0021] Step S124: Calculate the surface slope aspect based on the digital elevation model to obtain surface slope aspect data;

[0022] Step S125: Perform overlay analysis based on surface slope data and surface aspect data to obtain data on abrupt changes in terrain;

[0023] Step S126: Obtain comprehensive logging engineering area data;

[0024] Step S127: Divide the integrated logging project area data into regions based on the data of drastic terrain changes, thereby obtaining data of regions with drastic terrain changes;

[0025] Step S128: Draw a topographic profile based on the digital elevation model to obtain a topographic profile, and identify the fault line area based on the topographic profile to obtain fault line area data.

[0026] Step S129: Perform fault region intersection calculation based on fault line area data and abrupt terrain area data to obtain fault region data, and perform fault dip angle structure analysis based on fault region data to obtain fault structure data.

[0027] This invention employs point cloud high reflectivity filtering on lidar data to remove inaccurate data caused by noise, vegetation, or other interference, thereby obtaining more accurate point cloud data. This data processing improves the accuracy of terrain and geological feature identification. By generating a digital elevation model (DEM), the changes in surface height can be accurately reflected, providing detailed terrain information for subsequent geological analysis. Calculations of surface slope and aspect based on the DEM further refine the spatial distribution of terrain features, helping to identify risk areas in reservoirs, such as steep slopes or areas of abrupt terrain changes. Overlay analysis effectively identifies areas of abrupt terrain changes, which are often closely related to complex geological structures (such as faults and folds) and are key areas for oil and gas reservoir exploration. Regional division of these abruptly changing areas using integrated well logging data further helps determine the location and extent of oil and gas reservoirs, thereby improving the accuracy of oil and gas resource location and exploration efficiency. Furthermore, drawing terrain profiles provides more intuitive support for identifying fault lines, helping to accurately locate fault zones within reservoirs, which is crucial for understanding the distribution and flow paths of oil and gas. By performing intersection operations on fault region data, the spatial distribution of faults and their relationship with other geological structures can be further determined, aiding in fault dip structure analysis and providing a scientific basis for oil and gas reservoir development. The beneficial effects of this series of steps include improved data accuracy and efficiency in oil and gas exploration, the ability to update models in real time and adapt to complex geological environmental changes, and ensuring the accuracy and safety of oil and gas extraction.

[0028] Optionally, step S13 specifically includes:

[0029] Step S131: Perform non-ground point cloud separation on the lidar data to obtain ground point cloud data;

[0030] Step S132: Calculate the surface curvature based on the ground point cloud data to obtain the surface curvature data;

[0031] Step S133: Divide the surface curvature data into high curvature surface data and low curvature surface data.

[0032] Step S134: Identify negative curvature syncline regions in high curvature surface data to obtain syncline region data;

[0033] Step S135: Identify anticline regions with positive curvature in low curvature surface data to obtain anticline region data;

[0034] Step S136: Perform the intersection operation of the folded regions based on the syncline region data and the anticline region data to obtain the folded region data;

[0035] Step S137: Perform fold axis structure analysis based on fold area data to obtain fold structure data.

[0036] This invention effectively extracts real ground data from complex terrain by performing non-ground point cloud separation on lidar data, providing more accurate foundational data for terrain and geological analysis. By calculating surface curvature, the undulating characteristics of the terrain can be further identified, helping to describe the shape and structure of the surface and laying the foundation for subsequent geological structure analysis. Curvature division based on surface curvature allows for the separate extraction of high-curvature and low-curvature surfaces, enabling analysis of different terrain features and providing a more detailed perspective for geological structure identification. For high-curvature surface data, identifying negative-curvature syncline regions can accurately locate low-lying areas on the surface, which are associated with oil and gas reservoir accumulation zones. Conversely, identifying positive-curvature anticline regions in low-curvature surface data helps in locating anticline regions that form oil and gas reservoirs. By performing intersection operations on syncline and anticline region data, the spatial distribution of folded regions can be more accurately defined, identifying important geological structural features. Furthermore, by performing axial structure analysis on folded regions, potential fold structures within the reservoir can be revealed, providing crucial information for oil and gas reservoir exploration and development. The effectiveness of this series of steps is reflected in the ability to provide more refined and real-time updated three-dimensional data models for oil and gas exploration in complex geological environments, greatly improving the accuracy and efficiency of exploration and facilitating more precise oil and gas resource location and assessment in areas with complex geological environments.

[0037] Optionally, step S2 specifically includes:

[0038] Step S21: Extract multispectral features of the integrated logging project based on the remote sensing data of the integrated logging project monitoring, thereby obtaining multispectral data of the integrated logging project;

[0039] Step S22: Perform surface lithological spectral reflectance analysis on the multispectral data of the integrated logging project to obtain surface lithological spectral reflectance data;

[0040] Step S23: Analyze the surface lithological absorption zones of the multispectral data from the integrated logging project to obtain surface lithological absorption zone data;

[0041] Step S24: Integrate surface lithological characteristics based on surface lithological spectral reflectance data and surface lithological absorption zone data to obtain surface lithological data.

[0042] This invention extracts multispectral features from integrated well logging monitoring remote sensing data, revealing spectral information across different bands and providing more comprehensive surface information for subsequent geological analysis. This process not only accurately captures the optical properties of the surface but also offers high spatial resolution. Surface lithological spectral reflectance analysis based on this multispectral data helps to deeply understand the characteristics of surface lithological reflection, laying the foundation for accurate lithological distribution identification. This analysis can reveal the reflection patterns of different rock types, helping to quickly identify the main lithological components in reservoirs. Further analysis of surface lithological absorption zones, by identifying the characteristics of these zones, reveals the absorption characteristics of lithology in different bands, which is crucial for accurately determining reservoir composition and identifying favorable oil and gas reservoir areas. By integrating surface lithological spectral reflectance data and absorption zone data, a comprehensive description of reservoir lithological characteristics can be achieved, making geological models more accurate and providing clearer and more scientific guidance for subsequent oil and gas reservoir assessment, exploration, and development. These steps, through multi-angle analysis of lithological data, not only improve the accuracy of oil and gas exploration but also ensure efficient application in complex geological environments.

[0043] Optionally, step S22 specifically includes:

[0044] Step S221: Extract shortwave infrared band features from the multispectral data of the integrated logging project to obtain shortwave infrared band data;

[0045] Step S222: Obtain lithological absorption zone data and lithological spectral data;

[0046] Step S223: Based on the lithological absorption zone data, the shortwave infrared band data is divided into rock types to obtain rock type data;

[0047] Step S224: Perform spectral matching between lithological spectral data and rock type data to obtain rock spectral matching data;

[0048] Step S225: Draw a surface lithology classification map based on the rock spectral matching data of the integrated logging project area data to obtain the surface lithology classification map;

[0049] Step S226: Calculate the spectral reflectance based on the surface lithology classification map to obtain the surface lithology spectral reflectance data.

[0050] This invention extracts more detailed lithological information from multispectral data of integrated well logging projects using shortwave infrared (SIR) band features. In particular, the mineral characteristics of rocks are clearly revealed in the SIR band, aiding in the differentiation of different rock types. SIR band data provides rich spectral information for subsequent rock type classification, which, combined with lithological absorption band data, allows for precise rock type classification. This process ensures the accuracy of rock classification and provides strong support for further geological analysis. Spectral matching based on lithological spectral data and rock type data more accurately reflects the spectral characteristics of rocks, thereby improving the accuracy of rock classification results. This spectral matching process effectively reduces human interference and errors in traditional methods, improving data reliability. After obtaining the rock spectral matching data, drawing a surface lithological classification map visually displays the spatial distribution of various rock types within a region, helping to better understand the lithological composition and spatial structure of reservoirs. Simultaneously, spectral reflectance calculation based on the surface lithological classification map provides quantitative data support for geological analysis, further improving the accuracy and practicality of the geological model. Ultimately, the implementation of this series of steps improves the accuracy and efficiency of oil and gas exploration, especially in complex geological environments, providing more accurate surface lithology information and supporting efficient oil and gas reservoir assessment and exploitation decisions.

[0051] Optionally, step S23 specifically includes:

[0052] Step S231: Extract near-infrared band features from the multispectral data of the integrated logging project to obtain near-infrared band data;

[0053] Step S232: Divide the absorption peak range of iron minerals into near-infrared band data to obtain the absorption peak range data of iron minerals;

[0054] Step S233: Detect the absorption bands of the iron mineral absorption peak range data to obtain the iron mineral absorption band data;

[0055] Step S234: Calculate the absorption peak depth based on the iron mineral absorption band data to obtain the absorption peak depth data;

[0056] Step S235: Calculate the absorption peak width based on the iron mineral absorption band data to obtain the absorption peak width data;

[0057] Step S236: Calculate the iron mineral content based on the absorption peak depth data and absorption peak width data to obtain the iron mineral content data;

[0058] Step S237: Identify the wavelength position based on the iron mineral absorption band data to obtain wavelength position data;

[0059] Step S238: Based on wavelength location data and iron mineral content data, the surface lithological absorption zone characteristics are fused to obtain surface lithological absorption zone data.

[0060] This invention extracts near-infrared features from multispectral data from integrated well logging projects, enabling more precise capture of the spectral characteristics of rocks and minerals, particularly those related to iron minerals. Near-infrared data helps identify the distribution and characteristics of iron minerals in rocks, providing crucial data for subsequent iron mineral analysis. After dividing the near-infrared data into iron mineral absorption peak ranges, the absorption characteristic regions of iron minerals can be accurately located, providing more targeted references for mineral analysis. By detecting absorption bands, spectral absorption band data of iron minerals can be further extracted, aiding in the analysis of the types and distribution of iron minerals in rocks. Calculating the depth and width of absorption peaks based on iron mineral absorption band data can reveal the abundance and distribution characteristics of iron minerals, which is significant for assessing the iron mineral content in reservoirs. Iron mineral content data calculated from absorption peak depth and width data can more accurately quantify the iron mineral composition in reservoirs, providing crucial mineral information for oil and gas exploration. Identifying the wavelength positions of iron mineral absorption band data further enhances the ability to identify different rock formations and minerals, helping to clarify the types and distribution areas of minerals. Ultimately, combining wavelength location data with iron mineral content data to fuse surface lithological absorption zone characteristics provides a more comprehensive lithological analysis, improving the accuracy and effectiveness of geological exploration. These steps enable more precise application of oil and gas exploration in complex geological environments, while providing more reliable data support for reservoir assessment and exploitation decisions.

[0061] Optionally, step S4 specifically includes:

[0062] Step S41: Obtain oil and gas reservoir temperature sensing data, and draw a temperature gradient map based on the oil and gas reservoir temperature sensing data to obtain an oil and gas reservoir temperature gradient map.

[0063] Step S42: Identify high-temperature regions in the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature gradient map, thereby obtaining spatial distribution data of high-temperature oil and gas reservoirs.

[0064] Step S43: Obtain the geological thermal expansion coefficient of the oil and gas reservoir and the baseline temperature distribution data of the oil and gas reservoir;

[0065] Step S44: Calculate the temperature change of the spatial distribution data of high-temperature oil and gas reservoirs based on the oil and gas reservoir temperature benchmark distribution data to obtain oil and gas reservoir temperature change data.

[0066] Step S45: Calculate the thermal strain based on the oil and gas reservoir temperature change data and the geological thermal expansion coefficient of the oil and gas reservoir to obtain the oil and gas reservoir thermal strain data.

[0067] Step S46: Identify stress concentration areas in the thermal strain data of the oil and gas reservoir to obtain reservoir deformation data;

[0068] Step S47: Perform reservoir rock stability analysis based on reservoir deformation data to obtain reservoir rock stability data.

[0069] This invention acquires temperature sensing data from oil and gas reservoirs and plots temperature gradient maps, visually demonstrating the spatial distribution of reservoir temperature and helping explorers identify areas with significant temperature differences. Identifying high-temperature areas through temperature gradient maps effectively marks reservoir regions with anomalous heat zones, providing guidance for subsequent oil and gas extraction, particularly in assessing the impact of high-temperature areas on reservoir development. Combining oil and gas reservoir geological thermal expansion coefficient and temperature baseline distribution data allows for more accurate calculations of reservoir temperature changes, further revealing the impact of temperature fluctuations on reservoir properties. In this process, temperature change data provides a foundation for subsequent thermal strain calculations, helping to reveal the reservoir's mechanical response to changes in the thermal environment. Analysis of thermal strain data effectively identifies stress concentration areas in the reservoir, promptly identifying structural risk areas and reducing engineering challenges encountered during extraction. Finally, by performing rock formation stability analysis using reservoir deformation data, the stability of reservoir rock formations under thermal strain can be assessed, predicting risks under different extraction conditions and effectively improving the safety and feasibility of oil and gas exploration and extraction. This series of steps, by comprehensively considering temperature, coefficient of thermal expansion, and stress changes, enables a more comprehensive and accurate assessment of the deformation behavior of oil and gas reservoirs, and optimizes oil and gas extraction strategies.

[0070] Optionally, step S47 specifically includes:

[0071] Step S471: Draw a stress field distribution map based on the reservoir deformation data to obtain the stress field distribution map;

[0072] Step S472: Obtain reservoir rock compressive strength data;

[0073] Step S473: Calculate the reservoir area safety factor based on the reservoir rock compressive strength data and stress field distribution map;

[0074] Step S474: Perform a stability assessment on the reservoir deformation data based on the reservoir area safety factor to obtain reservoir rock stability data.

[0075] This invention utilizes reservoir deformation data to create stress field distribution maps, visually displaying the stress distribution within the reservoir. This helps explorers identify areas of stress concentration or uneven distribution, providing a visualization tool for reservoir stability analysis. Furthermore, obtaining compressive strength data of the reservoir rocks provides key parameters for the reservoir's physical properties, offering a scientific basis for subsequent safety assessments and exploitation decisions. By combining stress field distribution maps and compressive strength data to calculate reservoir area safety factors, the potential damage risks encountered during exploitation can be effectively assessed, identifying areas with potential structural problems, thereby optimizing exploitation plans and mitigating potential safety hazards. Finally, stability assessments based on reservoir deformation data using reservoir area safety factors help determine the stability of reservoir strata under different stress and environmental conditions, providing safety assurance for oil and gas exploitation and enabling real-time adjustments to exploitation strategies to ensure efficient exploitation and long-term sustainability in various reservoir environments.

[0076] Optionally, this specification also provides a system for constructing a three-dimensional model of an integrated logging project, used to execute a method for constructing a three-dimensional model of an integrated logging project as described above. This system includes:

[0077] Geological structure analysis module: used to acquire remote sensing data of integrated logging engineering monitoring, and to perform geological structure analysis based on the integrated logging engineering monitoring remote sensing data, thereby obtaining geological structure data;

[0078] Surface lithology identification module: used to extract multispectral features of the integrated well logging project based on the integrated well logging project monitoring remote sensing data, thereby obtaining multispectral data of the integrated well logging project; and to identify surface lithology based on the multispectral data of the integrated well logging project, thereby obtaining surface lithology data.

[0079] Oil and gas reservoir spatial distribution analysis module: used to construct a comprehensive three-dimensional model of the well logging project based on geological structure data and surface lithology data, thereby obtaining the comprehensive three-dimensional model of the well logging project; and to perform spatial distribution analysis of oil and gas reservoirs based on the comprehensive three-dimensional model of the well logging project, thereby obtaining spatial distribution data of oil and gas reservoirs;

[0080] The reservoir stability analysis module is used to acquire oil and gas reservoir temperature sensing data, and to perform reservoir deformation analysis on the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature sensing data, thereby obtaining reservoir deformation data; and to perform reservoir stability analysis on the reservoir deformation data, thereby obtaining reservoir stability data.

[0081] The integrated logging 3D model path optimization module is used to design drilling paths based on reservoir stability data to obtain drilling path data; it then optimizes the path of the integrated logging engineering 3D model based on the drilling path data to obtain an optimized 3D model of the integrated logging engineering, and uploads it to the integrated logging engineering management platform to perform model optimization tasks.

[0082] The present invention relates to a system for constructing a three-dimensional model of an integrated logging project. This system can realize the method for constructing any three-dimensional model of an integrated logging project according to the present invention. It is used to connect the operation and signal transmission media between various modules to complete the construction method of the three-dimensional model of the integrated logging project. The modules within the system cooperate with each other to construct a dynamically updated and accurate three-dimensional model, providing comprehensive support for the development and management of oil and gas fields. Attached Figure Description

[0083] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0084] Figure 1 This is a flowchart illustrating the steps of the method for constructing a three-dimensional model of a comprehensive logging project according to the present invention.

[0085] Figure 2 This is a detailed flowchart of step S1 in the present invention;

[0086] Figure 3 This is a detailed flowchart of step S13 in the present invention;

[0087] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0088] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0089] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0090] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0091] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for constructing a three-dimensional model of a comprehensive logging project, the method comprising the following steps:

[0092] Step S1: Obtain remote sensing data for integrated well logging engineering monitoring, and perform geological structure analysis based on the integrated well logging engineering monitoring remote sensing data to obtain geological structure data;

[0093] In this embodiment, the acquisition of integrated well logging engineering monitoring remote sensing data can be achieved through a high-resolution satellite imaging system or a UAV equipped with a multispectral camera. The remote sensing data should include spectral, thermal infrared, and topographic data. This data is preprocessed using remote sensing image processing software, including radiometric correction, atmospheric correction, and geometric correction. Image registration technology is used to match the remote sensing data with known geographic coordinates to ensure spatial positioning accuracy. Geological structure analysis is performed based on image processing algorithms for edge detection and morphological operations to extract structural features such as geological fault lines and folds. Fourier transform or wavelet transform is used to perform spectral analysis on the data to extract the ripples and orientation of rock strata. Finally, a detailed geological structure dataset is constructed through rock classification and geological texture analysis.

[0094] Step S2: Extract multispectral features from the integrated well logging project monitoring remote sensing data to obtain multispectral data of the integrated well logging project; identify surface lithology based on the multispectral data of the integrated well logging project to obtain surface lithology data.

[0095] In this embodiment, multispectral feature extraction for integrated logging engineering can be performed using multispectral analysis software. The core steps of data extraction include filtering and feature enhancement for different bands. Specifically, a bandpass filter is used to decompose the multispectral data into different bands such as infrared, visible light, and ultraviolet. Each band of data is segmented using the Otsu's algorithm (maximum inter-class variance) to identify different lithological characteristics. Surface lithology identification is based on reflectance spectral matching technology, employing a spectral library comparison method to match measured spectral data with standard lithological spectra to determine the lithology category. Spectral reflectance thresholds (e.g., 0.4-0.8) and absorption peak positions need to be considered to accurately distinguish geological components such as sandstone, shale, and limestone.

[0096] Step S3: Construct a three-dimensional model of the integrated logging project based on geological structure data and surface lithology data to obtain the three-dimensional model of the integrated logging project; perform spatial distribution analysis of oil and gas reservoirs based on the three-dimensional model of the integrated logging project to obtain spatial distribution data of oil and gas reservoirs.

[0097] In this embodiment, constructing a comprehensive 3D model of the well logging project requires integrating multi-source data. Geological structure data and surface lithology data are input into 3D modeling software (such as Petrel or GOCAD). Through mesh generation, the model is spatially fitted using the Discrete Element Method (DEM) and Finite Element Analysis (FEM). In the 3D model, the geological properties of each grid point need to be filled using spatial interpolation methods (such as Kriging interpolation) to obtain a high-precision reservoir model. The spatial distribution analysis of oil and gas reservoirs is then performed using reservoir evaluation software, combined with the model data, to calculate reservoir thickness, porosity, and permeability, forming spatial distribution data.

[0098] Step S4: Acquire oil and gas reservoir temperature sensing data, and perform reservoir deformation analysis on the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature sensing data to obtain reservoir deformation data; perform reservoir rock stability analysis based on the reservoir deformation data to obtain reservoir rock stability data.

[0099] In this embodiment, acquiring reservoir temperature sensing data requires deploying downhole temperature sensors. These sensors should be positioned at different depths and locations to obtain comprehensive temperature profile data. Temperature data is transmitted to the analysis platform via a data acquisition system (such as SCADA). Reservoir deformation analysis utilizes reservoir spatial distribution data, combined with the coefficient of thermal expansion and temperature change formulas. Deformation analysis is performed using numerical simulation software (such as ANSYS), employing a thermo-mechanical coupling model to simulate reservoir expansion and contraction behavior. Key input parameters include the coefficient of thermal expansion (e.g., 10^-5 / ℃) and the initial reservoir temperature. Stress analysis uses the Von Mises criterion to detect potential instability zones, ensuring the data fully characterizes reservoir deformation.

[0100] Step S5: Design the drilling path based on the reservoir rock stability data to obtain drilling path data; optimize the path of the integrated logging engineering 3D model based on the drilling path data to obtain the integrated logging engineering 3D optimized model, and upload it to the integrated logging engineering management platform to execute the model optimization task.

[0101] In this embodiment, the drilling path design utilizes 3D model data and reservoir stability data, employing a path optimization algorithm. Specific algorithms include Dijkstra's algorithm, used to plan the shortest path and avoid instability zones. Path calculations consider parameters such as well inclination angle, azimuth angle, and the direction of maximum horizontal stress. The drilling inclination angle range (e.g., 10°-60°) and maximum offset distance are set. The optimized path is verified and iterated using a path correction tool against the 3D model. After path optimization, the results are uploaded to the integrated logging engineering management platform for remote monitoring and dynamic adjustment of the path planning. Finally, the model and path data are uploaded and synchronized via API interface or file transfer protocol (FTP).

[0102] Optionally, step S1 specifically includes:

[0103] Step S11: Obtain the integrated logging engineering monitoring remote sensing data, and extract lidar features based on the integrated logging engineering monitoring remote sensing data to obtain lidar data;

[0104] In this embodiment, the acquisition of integrated well logging engineering monitoring remote sensing data is performed using a high-resolution remote sensing satellite and an unmanned aerial vehicle (UAV) LiDAR system. The LiDAR system should use pulsed laser ranging, transmitting and receiving laser pulses at nanosecond intervals to form high-density point cloud data. This data is synchronously recorded through a receiver and positioning system (such as GPS and an inertial measurement unit, IMU) to ensure spatial positioning accuracy. LiDAR feature extraction is performed using point cloud processing software (such as CloudCompare or LAStools), including data preprocessing steps such as denoising, coordinate transformation, and elevation standardization. Through algorithms based on gradient calculation and curvature analysis, irregular landforms and geological features are separated from the point cloud, ultimately outputting LiDAR data.

[0105] Step S12: Perform fault structure identification on the lidar data to obtain fault structure data;

[0106] In this embodiment, fault structure identification is based on topographic discontinuities and elevation differences in LiDAR data. Specific operations include using edge detection algorithms (such as the Canny algorithm) and fracture identification algorithms to identify fault lines in the point cloud. Regarding parameter settings, the elevation difference threshold should be set between 0.5 and 2 meters to distinguish between major faults and small fractures. The fault dip and strike are calculated by fitting a plane and normal vector, and the RANSAC algorithm is used to improve the robustness of identification. This process combines the overlay of high-resolution imagery and LiDAR data to enhance identification accuracy, and performs spatial coordinate correction and fault labeling.

[0107] Step S13: Perform wrinkle structure identification on the lidar data to obtain wrinkle structure data;

[0108] In this embodiment, fold structure identification is performed using a 3D terrain model extracted from LiDAR data. Fold identification employs geometric morphology algorithms, including curvature analysis based on the second derivative. By calculating the principal curvature and Gaussian curvature of the LiDAR data points, the location and trend of the fold axis are determined. A curvature threshold (e.g., 1 to 3 degrees / m) is set to distinguish significant folds from surface undulations to avoid false positives. Next, a template matching algorithm is used to compare the identification results with known fold models to confirm the type and geometry of the folds. Finally, the data is integrated and output as structural data containing fold orientation, amplitude, and frequency.

[0109] Step S14: Merge the geological structures based on the fault structure data and fold structure data to obtain the geological structure data.

[0110] In this embodiment, the merging of fault structure data and fold structure data is based on a geological information integration method. A GIS platform (such as ArcGIS or QGIS) is used for data layer overlay to ensure precise spatial alignment of the two types of data. During data merging, Boolean operations are used to mark overlapping areas as composite structure regions. Consistency checks are performed on the merged data, and spatial topology analysis is used to detect overlaps and adjacencies. To improve analysis accuracy, a digital elevation model (DEM) is used to spatially correct the merged data. The output is comprehensive geological structure data, annotating the location and geometric features of various geological characteristics for further geological analysis and engineering applications.

[0111] Optionally, step S12 specifically includes:

[0112] Step S121: Perform point cloud high reflectivity filtering on the lidar data to obtain point cloud filtered data;

[0113] In this embodiment, high reflectivity filtering of the LiDAR data point cloud is performed using a data preprocessing tool, employing a light intensity threshold filtering method. The light intensity threshold should be set within the range of 80 to 120 units, with the specific value determined based on the laser wavelength (e.g., 905nm or 1550nm) and ambient lighting conditions. Abnormally high reflectivity points, originating from reflections from building surfaces or metallic objects, are removed from the point cloud data using filtering algorithms (e.g., Gaussian filtering or median filtering). The preprocessed point cloud data is then converted into two-dimensional and three-dimensional formats to form filtered point cloud data, which is used for subsequent terrain analysis and model generation.

[0114] Step S122: Generate a digital elevation model based on the filtered point cloud data to obtain the digital elevation model;

[0115] In this embodiment, filtered point cloud data is used to generate a digital elevation model (DEM). This process uses interpolation methods (such as least squares or Kriging interpolation) to grid the point cloud, generating elevation data. The grid resolution of the DEM is selected from 1 to 5 meters, with the specific value set according to the size of the survey area and the level of detail required. The generation process requires processing the point cloud data using Python's GDAL library or ArcGIS's "Create DEM" tool, outputting gridded elevation data for analyzing surface features.

[0116] Step S123: Calculate the surface slope based on the digital elevation model to obtain surface slope data;

[0117] In this embodiment, the surface slope is calculated based on the generated DEM using a gradient algorithm based on digital elevation modeling. This calculation employs derivative calculation or Sobel filtering, obtaining the slope value by detecting elevation changes in each grid cell. The output slope data is represented in degrees, typically ranging from 0° to 90°, and is implemented using the "Slope Analysis" tool in the GIS platform. During the calculation process, data smoothness and the absence of null values ​​are ensured through angle smoothing using a compensation algorithm to eliminate errors.

[0118] Step S124: Calculate the surface slope aspect based on the digital elevation model to obtain surface slope aspect data;

[0119] In this embodiment, surface slope aspect calculation is based on DEM and slope data. The slope aspect of each grid cell is determined using directional derivatives and the "Aspect" analysis tool in ArcGIS. The slope aspect is expressed as an angle, ranging from 0° (north) to 360°, representing the direction of decrease in grid elevation. The surface slope aspect calculation results are output as raster data, with each pixel corresponding to a direction angle. This data is checked to ensure slope aspect consistency and areas with significant slope aspect differences are marked.

[0120] Step S125: Perform overlay analysis based on surface slope data and surface aspect data to obtain data on abrupt changes in terrain;

[0121] In this embodiment, the overlap analysis of surface slope data and surface aspect data is implemented through the "overlap analysis" module of GIS software, performing intersection and difference analysis. Specific thresholds are set, such as areas with a slope greater than 30° and aspect differences exceeding 45°, marking them as areas of abrupt change. This analysis is implemented through spatial queries and Boolean logic, outputting these marked areas as a new data layer, forming data on abrupt topographic changes.

[0122] Step S126: Obtain comprehensive logging engineering area data;

[0123] In this embodiment, high-precision surveying data and existing integrated well logging geological information databases are used to acquire data for the integrated well logging project area. This step combines field measurement data and remote sensing monitoring results to divide the well logging project area into different exploration sub-areas, forming multi-dimensional data tables and spatial coordinate sets to ensure the consistency and spatial matching of subsequent operation data.

[0124] Step S127: Divide the integrated logging project area data into regions based on the data of drastic terrain changes, thereby obtaining data of regions with drastic terrain changes;

[0125] In this embodiment, the integrated logging project area data is divided into regions based on data showing rapid terrain changes. Spatial analysis methods and clustering algorithms (such as K-Means or DBSCAN) are used to separate rapidly changing areas from gently changing areas. The data is spatially segmented, and the data layer of rapidly changing areas is separated using the "zoning statistics" function of a GIS tool. The segmented regional data is labeled with geographic coordinates and features for engineering planning and further surveying.

[0126] Step S128: Draw a topographic profile based on the digital elevation model to obtain a topographic profile, and identify the fault line area based on the topographic profile to obtain fault line area data.

[0127] In this embodiment, a digital elevation model (DEM) is used to draw topographic profiles. Profile drawing is achieved using GIS software or the Matplotlib library, by selecting specific profile lines and cutting them to generate the profile. A point-by-point interpolation algorithm based on the elevation grid is used to ensure accuracy during profile drawing. Edge detection algorithms (such as Sobel or Canny) are applied to identify break lines based on the profile, and the edge locations are output as break line region data.

[0128] Step S129: Perform fault region intersection calculation based on fault line area data and abrupt terrain area data to obtain fault region data, and perform fault dip angle structure analysis based on fault region data to obtain fault structure data.

[0129] In this embodiment, the intersection operation between the fault line area data and the data of areas with abrupt topographic changes is performed using Boolean logic operations to ensure that the identified fault areas overlap or are close to areas of abrupt topographic changes. The intersection operation is performed using the "Intersection" function in ArcGIS or the GeoPandas library in Python. Based on the intersection results, dip structure analysis is performed, and the dip angle and strike of the fault are determined using a fitting algorithm and normal vector analysis. The output fault structure data is labeled with dip angle, location, and direction information.

[0130] Optionally, step S13 specifically includes:

[0131] Step S131: Perform non-ground point cloud separation on the lidar data to obtain ground point cloud data;

[0132] In this embodiment, the separation of non-ground point clouds from LiDAR data is achieved using a rule-based classification algorithm. First, a filtering algorithm (such as a progressive filter or CSF filter) is applied to distinguish between non-ground and ground points. Specifically, the filtering threshold is set between 1.5 meters and 3 meters to ensure the removal of non-ground elements such as vegetation and buildings. Spatial density analysis is then performed on the point cloud, using nearest neighbor distance and elevation variation for filtering. The marked ground points are output as ground point cloud data. This process is completed using LiDAR data processing software (such as LASTools or the PDAL library).

[0133] Step S132: Calculate the surface curvature based on the ground point cloud data to obtain the surface curvature data;

[0134] In this embodiment, ground point cloud data is used to calculate surface curvature. The calculation method employs triangular mesh (TIN) construction and second-order derivative analysis. TIN construction is performed using a triangulation algorithm (such as Delaunay triangulation), and the curvature of each triangular mesh is obtained by fitting a quadratic surface and calculating its principal curvature. Curvature data is stored in raster format, with each pixel value corresponding to the curvature value at that point, ranging from negative to positive. This calculation process is implemented using GIS analysis tools (such as the "Curvature Analysis" tool in ArcGIS).

[0135] Step S133: Divide the surface curvature data into high curvature surface data and low curvature surface data.

[0136] In this embodiment, surface curvature data is divided into high-curvature and low-curvature regions. The standard deviation method or a fixed threshold (e.g., ±0.05 curvature units) is used to classify the curvature data, marking regions with absolute curvature values ​​greater than the threshold as high curvature and those below as low curvature. The division process utilizes Python scripts for data segmentation and labeling, and raster computing tools are used to generate high-curvature and low-curvature surface data.

[0137] Step S134: Identify negative curvature syncline regions in high curvature surface data to obtain syncline region data;

[0138] In this embodiment, negative curvature syncline regions are identified from high-curvature surface data by judging the sign and spatial distribution of curvature values. A negative value filter is used to extract all negative curvature regions, and the continuity and scale of curvature values ​​are further analyzed to ensure that the connectivity of the region meets the region determination criteria (e.g., continuity greater than 100 meters). The output syncline region data includes its geographic boundaries and curvature features, and its accuracy is verified using spatial statistical tools.

[0139] Step S135: Identify anticline regions with positive curvature in low curvature surface data to obtain anticline region data;

[0140] In this embodiment, positive curvature anticline regions are identified from low-curvature surface data using a method similar to that used for syncline regions. Regions with positive curvature are filtered, and area and morphological rules are applied to determine the anticline regions. The curvature range for region identification is set between 0.01 and 0.1 to ensure the stability of this curvature feature. The "region growth" tool in GIS software is used to extract regions that meet the criteria, outputting the anticline region data and labeling its morphological features.

[0141] Step S136: Perform the intersection operation of the folded regions based on the syncline region data and the anticline region data to obtain the folded region data;

[0142] In this embodiment, the intersection of syncline and anticline region data is performed, and Boolean operations are applied for cross-analysis of the data layer. The identified syncline and anticline regions are overlaid using the "intersection" function in the GIS platform or the spatial query function of the GeoPandas library to form folded region data. This process ensures the spatial accuracy of the overlapping areas and outputs folded region data containing boundaries, area, and location coordinates in vector data format.

[0143] Step S137: Perform fold axis structure analysis based on fold area data to obtain fold structure data.

[0144] In this embodiment, fold region data is used for fold axis structure analysis. The locations of the fold axes are extracted using profile lines and gradient analysis methods from a digital elevation model. Profile analysis employs the rate of curvature change method to calculate the axis of each fold, ensuring accurate identification of the axis's location and orientation. This analysis is implemented using ArcGIS's "Profile Tool" combined with a custom Python script, outputting fold structure data and indicating the direction, dip angle, and location information of the fold axes.

[0145] Optionally, step S2 specifically includes:

[0146] Step S21: Extract multispectral features of the integrated logging project based on the remote sensing data of the integrated logging project monitoring, thereby obtaining multispectral data of the integrated logging project;

[0147] In this embodiment, when extracting multispectral features from integrated well logging engineering monitoring remote sensing data, it is necessary to capture multi-band remote sensing data covering the logging area using hyperspectral imaging equipment, ensuring that each band has a complete spectral response. The hyperspectral equipment used should have at least 200 spectral bands, covering visible light, near-infrared, and short-wave infrared bands to ensure data integrity and accuracy. Data extraction is achieved through specific optical filters and high-resolution sensors, ensuring that the spectral reflectance of each pixel accurately represents the characteristics of the logging area. Spectral feature extraction needs to be combined with data preprocessing techniques, including radiometric correction and geometric correction, to eliminate atmospheric scattering and topographic effects. The extracted multispectral data is represented on the spectral map as characteristic spectral curves of different minerals and lithologies, clearly reflecting the reflectance differences of each band, forming a multispectral database, and providing a data foundation for subsequent surface lithology analysis.

[0148] Step S22: Perform surface lithological spectral reflectance analysis on the multispectral data of the integrated logging project to obtain surface lithological spectral reflectance data;

[0149] In this embodiment, when performing surface lithological spectral reflectance analysis on multispectral data from integrated well logging projects, spectral reflectance analysis software is used to compare the reflectance data of each band with a known lithological spectral library. During the analysis, it is necessary to ensure that the spectral library used covers common rock types within the integrated well logging area, such as sandstone, shale, and limestone. In spectral reflectance analysis, key spectral features, such as spectral characteristic peaks and depressions in the 350-2500 nm range, are relied upon to analyze reflectance variations. After standardizing the reflectance data, the normalized spectral reflectance value is calculated for each pixel to ensure that the analysis results can effectively identify rock types and spatial distribution. Using spectral calculation methods, such as spectral angle mapping (SAM), the reflectance data is converted into a lithological feature map, and the specific spectral characteristic peak positions and widths for each lithological category are recorded to generate accurate surface lithological spectral reflectance data.

[0150] Step S23: Analyze the surface lithological absorption zones of the multispectral data from the integrated logging project to obtain surface lithological absorption zone data;

[0151] In this embodiment, when analyzing surface lithological absorption bands from multispectral data of integrated well logging projects, it is necessary to identify key absorption bands and extract characteristic absorption peaks of the lithology, typically in the shortwave infrared band (1000-2500 nm). Using spectral analysis algorithms within this wavelength range, the positions, depths, and widths of characteristic absorption peaks of different minerals are detected, identifying absorption bands for minerals such as iron minerals, carbonates, and clay minerals. Absorption band detection requires curve fitting technology, employing the least squares method to fit the absorption band curves to improve detection accuracy. During the detection process, noise removal and spectral smoothing must be ensured to avoid the influence of interfering data on absorption band determination. The absorption band data will be displayed as an absorption coefficient diagram corresponding to each band, clearly indicating the absorption characteristic range of each mineral. The generated surface lithological absorption band data includes the specific wavelength positions of the absorption peaks and related characteristic information.

[0152] Step S24: Integrate surface lithological characteristics based on surface lithological spectral reflectance data and surface lithological absorption zone data to obtain surface lithological data.

[0153] In this embodiment, when integrating surface lithological features based on surface lithological spectral reflectance data and surface lithological absorption zone data, the two types of data are merged to generate comprehensive lithological feature data. The integration of spectral reflectance and absorption zone features requires a data fusion algorithm, such as the weighted average method, to ensure effective combination of different data sources. The data fusion calculation for each pixel considers spectral matching degree, prioritizing the integration of lithological information whose reflectance characteristics are consistent with the absorption zone. A clear threshold is set during the integration process, such as a spectral matching degree > 0.8, to determine whether they belong to the same lithology. The final generated surface lithological data should include rock type, main mineral composition, spectral characteristic parameters, and key feature descriptions of the absorption zone, facilitating subsequent geological analysis and lithological assignment in the 3D model.

[0154] Optionally, step S22 specifically includes:

[0155] Step S221: Extract shortwave infrared band features from the multispectral data of the integrated logging project to obtain shortwave infrared band data;

[0156] In this embodiment, when extracting short-wave infrared features from multispectral data of integrated well logging projects, a spectral processing system is required to perform band separation and extraction. The extraction range is set between 1000 and 2500 nanometers, covering the key bands of short-wave infrared. The raw data is preprocessed using spectral correction techniques, such as dark current compensation and linear regression smoothing, to eliminate noise and discontinuities. Then, a bandpass filter is used for spectral feature separation to ensure that the short-wave infrared data clearly presents the key mineral absorption characteristics. Data extraction must be performed at a fixed spectral resolution, such as 10 nanometers, to maintain high-precision analysis. The extracted data is saved as a short-wave infrared data file, containing the spectral reflectance information of each pixel, for subsequent analysis steps.

[0157] Step S222: Obtain lithological absorption zone data and lithological spectral data;

[0158] In this embodiment, during the acquisition of lithological absorption band data and lithological spectral data, high-precision spectral analysis software is used to decompose and identify absorption bands in the multispectral data. First, using shortwave infrared data, characteristic absorption bands of minerals are identified, and the center position, depth, and width parameters of the absorption bands are extracted. This process employs differential spectral analysis to accurately detect the minimum value and boundary of the characteristic bands. Lithological spectral data acquisition is based on a standardized spectral library containing the reflectance characteristics of common minerals in the logging area, such as quartz, feldspar, and clay minerals, with a wavelength range of 350 to 2500 nanometers. Cross-comparison with the lithological spectral library verifies the spectral characteristics of the data to ensure the accuracy and validity of the reflectance curves and absorption band information.

[0159] Step S223: Based on the lithological absorption zone data, the shortwave infrared band data is divided into rock types to obtain rock type data;

[0160] In this embodiment, when classifying shortwave infrared data into rock types based on lithological absorption band data, a spectral matching algorithm, such as the spectral angle mapping (SAM) method, is used to calculate the matching degree between the spectral feature vector of each pixel and the standard lithological absorption band. Specifically, each spectral vector is standardized, and a matching threshold of 0.9 is set to ensure high accuracy in data classification. Rock type classification divides the shortwave infrared data into different lithological regions, each region being labeled with a corresponding mineral type. The output is a rock type data file containing a spatial distribution map and lithological labeling information for each pixel, facilitating further lithological analysis and classification.

[0161] Step S224: Perform spectral matching between lithological spectral data and rock type data to obtain rock spectral matching data;

[0162] In this embodiment, when performing spectral matching between lithological spectral data and rock type data, a spectral matching algorithm, such as the least squares fitting method, is used to compare the spectral data pixel by pixel. The lithological spectral data for each pixel undergoes noise removal and spectral correction using spectral smoothing technology to ensure that the input spectral data matches the actual measurements. The matching results are presented as spectral difference values, and a matching threshold of <5% spectral error is defined. Points meeting the matching criteria are marked as successfully matched. The matching results generate a rock spectral matching data file, where each matched pixel is identified as the corresponding lithological type.

[0163] Step S225: Draw a surface lithology classification map based on the rock spectral matching data of the integrated logging project area data to obtain the surface lithology classification map;

[0164] In this embodiment, when drawing a surface lithology classification map of the integrated logging engineering area data based on rock spectral matching data, a GIS system is used for spatial data processing. The matching data is input into the GIS platform, and different lithology categories are labeled by pixel. A classification algorithm is applied to visualize the spatial data into a lithology classification map. Different colors and legends are used to identify rock categories in the map, such as red representing sandstone and blue representing shale. During drawing, the resolution is ensured to match the sampling points, such as using a resolution of 10 meters per pixel to maintain detail clarity. Each lithological region in the map is labeled with its boundary and area using vectorization technology, generating a lithology classification map with spatial reference.

[0165] Step S226: Calculate the spectral reflectance based on the surface lithology classification map to obtain the surface lithology spectral reflectance data.

[0166] In this embodiment, when calculating spectral reflectance based on the surface lithology classification map, average reflectance data needs to be extracted from each lithological region in the classification map. Using a spectral analysis tool, spectral reflectance data for each region is selected and sampled multiple times to ensure calculation accuracy. The sampling interval and sample size are set, such as sampling 1000 pixels per region, and the sampled values ​​are averaged to calculate the representative spectral reflectance of the region. The reflectance results are recorded in a spectral reflectance data table, where each lithology category is associated with its average reflectance value. This data is used to assess lithological characteristics and provide accurate spectral information for subsequent analysis.

[0167] Optionally, step S23 specifically includes:

[0168] Step S231: Extract near-infrared band features from the multispectral data of the integrated logging project to obtain near-infrared band data;

[0169] In this embodiment, when extracting near-infrared features from multispectral data of integrated well logging projects, a spectral analysis system is used to divide the data into spectral ranges. The near-infrared band range is set to 700 to 2500 nanometers. Preprocessing steps such as spectral smoothing and baseline correction are used to ensure data quality and continuity. Spectral denoising algorithms, such as wavelet transform, are employed to remove interference signals and highlight target features. Spectral segmentation technology is used to separate and extract the spectral data of each pixel. The processed near-infrared band data is stored as a spectral matrix file, containing reflectance information and spatial location of each sampling point, for subsequent analysis steps.

[0170] Step S232: Divide the absorption peak range of iron minerals into near-infrared band data to obtain the absorption peak range data of iron minerals;

[0171] In this embodiment, spectral decomposition technology is required when dividing the absorption peak range of iron minerals in near-infrared band data. The absorption peaks of iron minerals are typically in the range of 850 to 1200 nanometers. The absorption peak range is determined by identifying local minima and points of change in the curve slope in the spectral curve. A spectral segmentation algorithm, such as polynomial fitting, is used to accurately divide this range. This process sets an absorption intensity threshold, for example, points with reflectance below 15% as initial boundaries to ensure accurate range division. The segmentation result is output as absorption peak range data, marking the start and end points of wavelength for each region, providing a clear characteristic range of iron minerals.

[0172] Step S233: Detect the absorption bands of the iron mineral absorption peak range data to obtain the iron mineral absorption band data;

[0173] In this embodiment, when detecting absorption bands in the absorption peak range data of iron minerals, differential spectroscopy is used to differentiate the spectral curve and identify the depth and position of the absorption band. The center position of the absorption band is determined by finding the changes in the first and second derivatives of the spectral curve. A sensitivity threshold is set for detection, for example, an absorption band depth greater than 5% is used as an effective detection standard. Noise removal and correction are performed on the detected absorption band data to ensure data consistency and accuracy. The processing results are saved as an absorption band data file, providing absorption band parameters for each detection point, including the center wavelength and characteristic width.

[0174] Step S234: Calculate the absorption peak depth based on the iron mineral absorption band data to obtain the absorption peak depth data;

[0175] In this embodiment, when calculating the absorption peak depth based on the iron mineral absorption band data, an absorption depth algorithm is used to calculate the maximum absorption depth in the spectrum. The absorption depth is obtained by fitting the baseline of the spectral curve and measuring the vertical distance between the curve and the baseline. A standardized formula is used, such as D = 1 - Rmin / Rbase, where Rmin is the lowest reflectance of the absorption band and Rbase is the baseline reflectance. The calculation results are stored as an absorption peak depth data table, containing the depth parameter for each band.

[0176] Step S235: Calculate the absorption peak width based on the iron mineral absorption band data to obtain the absorption peak width data;

[0177] In this embodiment, when calculating the absorption peak width based on the iron mineral absorption band data, it is necessary to measure the wavelength span of the spectral curve. Using the center of the absorption band as the baseline, wavelength positions with reflectance close to the baseline on both sides are found to determine the full width at half maximum (FWHM) of the absorption peak. A spectral resolution of 10 nanometers is set to ensure the precision of the width measurement. The width data output is an absorption band width dataset, marking the start and end wavelengths of each absorption peak and appending the actual measured width value.

[0178] Step S236: Calculate the iron mineral content based on the absorption peak depth data and absorption peak width data to obtain the iron mineral content data;

[0179] In this embodiment, when calculating the iron mineral content based on the absorption peak depth and width data, an empirical formula is applied to convert the absorption parameters into mineral content values. The formula is as follows:

[0180] C = k × D × W;

[0181] Where k is the correction coefficient, D is the absorption depth, and W is the absorption width. The correction coefficient k is determined through experimental data, for example, by using a reference sample with known iron ore content for calibration. The calculation results are stored in an iron ore content data file, listing the percentage of iron ore content at each measurement point.

[0182] Step S237: Identify the wavelength position based on the iron mineral absorption band data to obtain wavelength position data;

[0183] In this embodiment, when identifying wavelength positions based on iron mineral absorption band data, a spectral analysis tool is used to detect the center wavelength position of each absorption band. Interpolation is performed on the spectral curves to identify the center position at a higher resolution. The calculation error range is set to be less than 0.5 nanometers to ensure the accuracy of wavelength identification. The wavelength position data is saved as a data table, providing the center wavelength position of the absorption peak at each sampling point and related information, facilitating subsequent geological analysis.

[0184] Step S238: Based on wavelength location data and iron mineral content data, the surface lithological absorption zone characteristics are fused to obtain surface lithological absorption zone data.

[0185] In this embodiment, when fusing surface lithological absorption zone characteristics based on wavelength location data and iron mineral content data, the two types of data are integrated using spatial analysis techniques. Through overlay analysis, the wavelength location data and content data are jointly statistically analyzed to generate an absorption zone feature map. This map uses color and numerical values ​​to identify the iron mineral content and wavelength characteristics at different locations. The fusion process utilizes spatial statistical tools from a GIS platform, such as Kriging interpolation, to enhance the detail, completeness, and geological significance of the data fusion. The result generates surface lithological absorption zone data, including the characteristics of each absorption zone and the distribution of mineral content.

[0186] Optionally, step S4 specifically includes:

[0187] Step S41: Obtain oil and gas reservoir temperature sensing data, and draw a temperature gradient map based on the oil and gas reservoir temperature sensing data to obtain an oil and gas reservoir temperature gradient map.

[0188] In this embodiment, a temperature sensor array with an accuracy of 0.1℃ is used for multi-point measurements during the acquisition of oil and gas reservoir temperature sensing data. Sensors are deployed at different depths and locations within the reservoir to ensure comprehensive spatial coverage of the data. The data acquisition system transmits temperature data in real time via fiber optic communication technology. The collected data undergoes temperature deviation correction and data smoothing to remove noise and measurement errors. Based on the processed data, GIS software is used to perform interpolation analysis of the temperature data, generating a temperature gradient map to display the temperature change trend within the oil and gas reservoir.

[0189] Step S42: Identify high-temperature regions in the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature gradient map, thereby obtaining spatial distribution data of high-temperature oil and gas reservoirs.

[0190] In this embodiment, when identifying high-temperature regions in the spatial distribution data of oil and gas reservoirs based on the temperature gradient map, a temperature stratification algorithm is used to partition the temperature changes in the map. A high-temperature threshold is set, for example, areas above 80°C are marked as high-temperature zones. Spatial clustering analysis is performed on the areas exceeding the threshold to identify the outline and area of ​​the high-temperature zones. The clustering results are visualized using the partitioning tool of the GIS platform, outputting spatial distribution data of high-temperature oil and gas reservoirs containing location and temperature information.

[0191] Step S43: Obtain the geological thermal expansion coefficient of the oil and gas reservoir and the baseline temperature distribution data of the oil and gas reservoir;

[0192] In this embodiment, when acquiring the geological thermal expansion coefficient and temperature baseline distribution data of oil and gas reservoirs, the thermal expansion coefficient data of rock samples measured in the laboratory are extracted from the geological exploration report, typically in units of 10^-6 / ℃. The temperature baseline distribution data is generated through historical measurement data or similar reservoir temperature models, indicating the baseline temperature values ​​of different regions in the reservoir. The data format is uniformly a gridded table, facilitating matching with spatial distribution data in subsequent calculations.

[0193] Step S44: Calculate the temperature change of the spatial distribution data of high-temperature oil and gas reservoirs based on the oil and gas reservoir temperature benchmark distribution data to obtain oil and gas reservoir temperature change data.

[0194] In this embodiment, a point-to-point data comparison method is used to calculate temperature changes in the spatial distribution data of high-temperature oil and gas reservoirs based on temperature baseline distribution data. The temperature change value within each grid cell is obtained by calculating the difference between the current temperature and the baseline temperature, using the formula ΔT = Tcurrent - Tbaseline. Data calculation is performed using automated scripts to ensure computational efficiency and accuracy when dealing with large amounts of data. The output temperature change data is stored in chart form, providing spatial location and temperature differences.

[0195] Step S45: Calculate the thermal strain based on the oil and gas reservoir temperature change data and the geological thermal expansion coefficient of the oil and gas reservoir to obtain the oil and gas reservoir thermal strain data.

[0196] In this embodiment, when calculating thermal strain based on oil and gas reservoir temperature variation data and geological thermal expansion coefficient, a linear thermal strain calculation formula is applied.

[0197] ∈=α×ΔT;

[0198] Where α is the coefficient of thermal expansion and ΔT is the temperature change. The calculation process uses a scripting language, such as Python, combined with the NumPy library for large-scale data processing. The strain value of each grid point is output into a matrix to form a reservoir thermal strain data map, which includes the spatial distribution of strain values.

[0199] Step S46: Identify stress concentration areas in the thermal strain data of the oil and gas reservoir to obtain reservoir deformation data;

[0200] In this embodiment, stress analysis software is used to identify high-strain concentration areas when analyzing thermal strain data of oil and gas reservoirs. A threshold for strain concentration is set; for example, areas with strain values ​​exceeding 2% are marked as stress concentration areas. During the analysis, finite element analysis is used, inputting strain data into the stress field calculation module to identify potential stress concentration points and their expansion directions. The output results are displayed as a spatial heatmap, showing the precise location and intensity distribution of high-stress areas.

[0201] Step S47: Perform reservoir rock stability analysis based on reservoir deformation data to obtain reservoir rock stability data.

[0202] In this embodiment, when performing reservoir stability analysis based on reservoir deformation data, a rock instability analysis model is used to calculate the compressive and shear strength of the rock layers under stress. Rock physical properties, such as Young's modulus and Poisson's ratio, from a geological parameter database are used for calculation. The reservoir deformation data is imported into a stability analysis tool to simulate deformation behavior under high stress conditions. The results are output as reservoir stability data, indicating the stability level of each region to help determine potential instability risks.

[0203] Optionally, step S47 specifically includes:

[0204] Step S471: Draw a stress field distribution map based on the reservoir deformation data to obtain the stress field distribution map;

[0205] In this embodiment, during the process of drawing a stress field distribution map based on reservoir deformation data, a finite element analysis tool is used to calculate and process the reservoir deformation data. The reservoir deformation data is input into the analysis software, and stress field simulation and calculation are performed by combining geological structural features and known boundary conditions. The direction and magnitude of the principal stresses in each region are output through the software's stress calculation module, and the stress field distribution map is drawn in GIS or professional drawing tools. The stress field distribution map displays the stress intensity and distribution in each region using different colors or contour lines, providing visualized stress information and helping to identify stress concentration points.

[0206] Step S472: Obtain reservoir rock compressive strength data;

[0207] In this embodiment, when acquiring reservoir rock compressive strength data, the compressive strength values ​​are extracted from rock sample data measured in the laboratory. Specifically, uniaxial compressive strength tests are conducted on different types of rock samples under standard conditions, and their maximum compressive strength is recorded. Compressive strength data is typically expressed in MPa (megapascals) to ensure accurate identification of strength data for rocks in different regions. Experimental data is organized through a database system and imported into the analysis tool for use in subsequent steps. If historical data is available, its consistency is verified to ensure the accuracy of the analysis.

[0208] Step S473: Calculate the reservoir area safety factor based on the reservoir rock compressive strength data and stress field distribution map;

[0209] In this embodiment, when calculating the safety factor of the reservoir area based on the reservoir rock compressive strength data and stress field distribution map, the safety factor calculation formula is adopted:

[0210]

[0211] Here, Compressive Strength is the maximum compressive stress that the rock can withstand, and Applied Stress is the stress experienced by the reservoir under actual conditions, typically provided by a stress field distribution map. The stress field distribution map provides the stress value at each grid location, and combined with the corresponding compressive strength data, the safety factor for each region is calculated. The calculation process is automated through scripts or analysis programs. The safety factor results are stored as two-dimensional or three-dimensional charts, outputting high-risk areas and their safety factor distributions to clearly identify areas where failure may occur.

[0212] Step S474: Perform a stability assessment on the reservoir deformation data based on the reservoir area safety factor to obtain reservoir rock stability data.

[0213] In this embodiment, when assessing the stability of reservoir deformation data based on the reservoir region safety factor, a reservoir rock layer stability analysis standard is applied. Regions with a safety factor less than 1.5 are typically marked as potentially unstable areas. Through hierarchical processing of the analysis results, regions within the safety factor range are divided into stable, metastable, and unstable areas. The stability assessment is verified using analysis tools combined with the historical deformation trends and current stress state of each region, generating reservoir rock layer stability data. The output data is provided in the form of visualizations and reports, ensuring the identification of reservoir areas requiring key attention and providing a safe rock layer assessment basis for template recognition welding operations of industrial robots.

[0214] Optionally, this specification also provides a system for constructing a three-dimensional model of a comprehensive logging project, used to execute the method for constructing a three-dimensional model of a comprehensive logging project as described above. The system for constructing a three-dimensional model of a comprehensive logging project includes:

[0215] Geological structure analysis module: used to acquire remote sensing data of integrated logging engineering monitoring, and to perform geological structure analysis based on the integrated logging engineering monitoring remote sensing data, thereby obtaining geological structure data;

[0216] Surface lithology identification module: used to extract multispectral features of the integrated well logging project based on the integrated well logging project monitoring remote sensing data, thereby obtaining multispectral data of the integrated well logging project; and to identify surface lithology based on the multispectral data of the integrated well logging project, thereby obtaining surface lithology data.

[0217] Oil and gas reservoir spatial distribution analysis module: used to construct a comprehensive three-dimensional model of the well logging project based on geological structure data and surface lithology data, thereby obtaining the comprehensive three-dimensional model of the well logging project; and to perform spatial distribution analysis of oil and gas reservoirs based on the comprehensive three-dimensional model of the well logging project, thereby obtaining spatial distribution data of oil and gas reservoirs;

[0218] The reservoir stability analysis module is used to acquire oil and gas reservoir temperature sensing data, and to perform reservoir deformation analysis on the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature sensing data, thereby obtaining reservoir deformation data; and to perform reservoir stability analysis on the reservoir deformation data, thereby obtaining reservoir stability data.

[0219] The integrated logging 3D model path optimization module is used to design drilling paths based on reservoir stability data to obtain drilling path data; it then optimizes the path of the integrated logging engineering 3D model based on the drilling path data to obtain an optimized 3D model of the integrated logging engineering, and uploads it to the integrated logging engineering management platform to perform model optimization tasks.

[0220] The present invention relates to a system for constructing a three-dimensional model of an integrated logging project. This system can realize the method for constructing any three-dimensional model of an integrated logging project according to the present invention. It is used to connect the operation and signal transmission media between various modules to complete the construction method of the three-dimensional model of the integrated logging project. The modules within the system cooperate with each other to construct a dynamically updated and accurate three-dimensional model, providing comprehensive support for the development and management of oil and gas fields.

[0221] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0222] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for constructing a three-dimensional model of a comprehensive logging project, characterized in that, Includes the following steps: Step S1: Obtain remote sensing data for integrated well logging engineering monitoring, and perform geological structure analysis based on the integrated well logging engineering monitoring remote sensing data to obtain geological structure data; Step S2: Extract multispectral features from the integrated well logging project monitoring remote sensing data to obtain multispectral data of the integrated well logging project; identify surface lithology based on the multispectral data of the integrated well logging project to obtain surface lithology data. Step S3: Construct a three-dimensional model of the integrated logging project based on geological structure data and surface lithology data to obtain the three-dimensional model of the integrated logging project; perform spatial distribution analysis of oil and gas reservoirs based on the three-dimensional model of the integrated logging project to obtain spatial distribution data of oil and gas reservoirs. Step S4: Obtain oil and gas reservoir temperature sensing data, and perform reservoir deformation analysis on the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature sensing data to obtain reservoir deformation data. Based on the reservoir deformation data, reservoir stability analysis is performed to obtain reservoir stability data. Step S4 specifically involves: Step S41: Obtain oil and gas reservoir temperature sensing data, and draw a temperature gradient map based on the oil and gas reservoir temperature sensing data to obtain an oil and gas reservoir temperature gradient map. Step S42: Identify high-temperature regions in the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature gradient map, thereby obtaining spatial distribution data of high-temperature oil and gas reservoirs. Step S43: Obtain the geological thermal expansion coefficient of the oil and gas reservoir and the baseline temperature distribution data of the oil and gas reservoir; Step S44: Calculate the temperature change of the spatial distribution data of high-temperature oil and gas reservoirs based on the oil and gas reservoir temperature benchmark distribution data to obtain oil and gas reservoir temperature change data. Step S45: Calculate the thermal strain based on the oil and gas reservoir temperature change data and the geological thermal expansion coefficient of the oil and gas reservoir to obtain the oil and gas reservoir thermal strain data. Step S46: Identify stress concentration areas in the thermal strain data of the oil and gas reservoir to obtain reservoir deformation data; Step S47: Perform reservoir rock stability analysis based on reservoir deformation data to obtain reservoir rock stability data; Step S5: Design the drilling path based on the reservoir rock stability data to obtain the drilling path data; Based on the drilling path data, the path of the three-dimensional model of the integrated logging project is optimized to obtain the optimized three-dimensional model of the integrated logging project, which is then uploaded to the integrated logging project management platform to perform the model optimization task. The optimization of the three-dimensional model of the integrated logging project based on drilling path data includes: drilling path design using the three-dimensional model and reservoir rock stability data, and using path optimization algorithms to plan the shortest path. In the shortest path calculation, the drilling inclination angle, azimuth angle and maximum horizontal stress direction parameters are combined to set the drilling inclination angle range and maximum offset distance. The optimized path is then verified and iterated on the three-dimensional model using a path correction tool.

2. The method for constructing a three-dimensional model of a comprehensive logging project according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the integrated logging engineering monitoring remote sensing data, and extract lidar features based on the integrated logging engineering monitoring remote sensing data to obtain lidar data; Step S12: Perform fault structure identification on the lidar data to obtain fault structure data; Step S13: Perform wrinkle structure identification on the lidar data to obtain wrinkle structure data; Step S14: Merge the geological structures based on the fault structure data and fold structure data to obtain the geological structure data.

3. The method for constructing a three-dimensional model of a comprehensive logging project according to claim 2, characterized in that, Step S12 is as follows: Step S121: Perform point cloud high reflectivity filtering on the lidar data to obtain point cloud filtered data; Step S122: Generate a digital elevation model based on the filtered point cloud data to obtain the digital elevation model; Step S123: Calculate the surface slope based on the digital elevation model to obtain surface slope data; Step S124: Calculate the surface slope aspect based on the digital elevation model to obtain surface slope aspect data; Step S125: Perform overlay analysis based on surface slope data and surface aspect data to obtain data on abrupt changes in terrain; Step S126: Obtain comprehensive logging engineering area data; Step S127: Divide the integrated logging project area data into regions based on the data of drastic terrain changes, thereby obtaining data of regions with drastic terrain changes; Step S128: Draw a topographic profile based on the digital elevation model to obtain a topographic profile, and identify the fault line area based on the topographic profile to obtain fault line area data. Step S129: Perform fault region intersection calculation based on fault line area data and abrupt terrain area data to obtain fault region data, and perform fault dip angle structure analysis based on fault region data to obtain fault structure data.

4. The method for constructing a three-dimensional model of a comprehensive logging project according to claim 2, characterized in that, Step S13 is as follows: Step S131: Perform non-ground point cloud separation on the lidar data to obtain ground point cloud data; Step S132: Calculate the surface curvature based on the ground point cloud data to obtain the surface curvature data; Step S133: Divide the surface curvature data into high curvature surface data and low curvature surface data. Step S134: Identify negative curvature syncline regions in high curvature surface data to obtain syncline region data; Step S135: Identify anticline regions with positive curvature in low curvature surface data to obtain anticline region data; Step S136: Perform the intersection operation of the folded regions based on the syncline region data and the anticline region data to obtain the folded region data; Step S137: Perform fold axis structure analysis based on fold area data to obtain fold structure data.

5. The method for constructing a three-dimensional model of a comprehensive logging project according to claim 1, characterized in that, Step S2 is as follows: Step S21: Extract multispectral features of the integrated logging project based on the remote sensing data of the integrated logging project monitoring, thereby obtaining multispectral data of the integrated logging project; Step S22: Perform surface lithological spectral reflectance analysis on the multispectral data of the integrated logging project to obtain surface lithological spectral reflectance data; Step S23: Analyze the surface lithological absorption zones of the multispectral data from the integrated logging project to obtain surface lithological absorption zone data; Step S24: Integrate surface lithological characteristics based on surface lithological spectral reflectance data and surface lithological absorption zone data to obtain surface lithological data.

6. The method for constructing a three-dimensional model of a comprehensive logging project according to claim 5, characterized in that, Step S22 is as follows: Step S221: Extract shortwave infrared band features from the multispectral data of the integrated logging project to obtain shortwave infrared band data; Step S222: Obtain lithological absorption zone data and lithological spectral data; Step S223: Based on the lithological absorption zone data, the shortwave infrared band data is divided into rock types to obtain rock type data; Step S224: Perform spectral matching between lithological spectral data and rock type data to obtain rock spectral matching data; Step S225: Draw a surface lithology classification map based on the rock spectral matching data of the integrated logging project area data to obtain the surface lithology classification map; Step S226: Calculate the spectral reflectance based on the surface lithology classification map to obtain surface lithology spectral reflectance data.

7. The method for constructing a three-dimensional model of a comprehensive logging project according to claim 5, characterized in that, Step S23 is as follows: Step S231: Extract near-infrared band features from the multispectral data of the integrated logging project to obtain near-infrared band data; Step S232: Divide the absorption peak range of iron minerals into near-infrared band data to obtain the absorption peak range data of iron minerals; Step S233: Detect the absorption bands of the iron mineral absorption peak range data to obtain the iron mineral absorption band data; Step S234: Calculate the absorption peak depth based on the iron mineral absorption band data to obtain the absorption peak depth data; Step S235: Calculate the absorption peak width based on the iron mineral absorption band data to obtain the absorption peak width data; Step S236: Calculate the iron mineral content based on the absorption peak depth data and absorption peak width data to obtain the iron mineral content data; Step S237: Identify the wavelength position based on the iron mineral absorption band data to obtain wavelength position data; Step S238: Based on wavelength location data and iron mineral content data, the surface lithological absorption zone characteristics are fused to obtain surface lithological absorption zone data.

8. The method for constructing a three-dimensional model of a comprehensive logging project according to claim 1, characterized in that, Step S4 is as follows: Step S41: Obtain oil and gas reservoir temperature sensing data, and draw a temperature gradient map based on the oil and gas reservoir temperature sensing data to obtain an oil and gas reservoir temperature gradient map. Step S42: Identify high-temperature regions in the spatial distribution data of oil and gas reservoirs based on the oil and gas reservoir temperature gradient map, thereby obtaining spatial distribution data of high-temperature oil and gas reservoirs. Step S43: Obtain the geological thermal expansion coefficient of the oil and gas reservoir and the baseline temperature distribution data of the oil and gas reservoir; Step S44: Calculate the temperature change of the spatial distribution data of high-temperature oil and gas reservoirs based on the oil and gas reservoir temperature benchmark distribution data to obtain oil and gas reservoir temperature change data. Step S45: Calculate the thermal strain based on the oil and gas reservoir temperature change data and the geological thermal expansion coefficient of the oil and gas reservoir to obtain the oil and gas reservoir thermal strain data. Step S46: Identify stress concentration areas in the thermal strain data of the oil and gas reservoir to obtain reservoir deformation data; Step S47: Perform reservoir rock stability analysis based on reservoir deformation data to obtain reservoir rock stability data.

9. A system for constructing a three-dimensional model of a comprehensive logging project, characterized in that, A method for constructing a three-dimensional model of an integrated logging project as described in claim 1, the system for constructing the three-dimensional model of the integrated logging project includes: Geological structure analysis module: used to acquire remote sensing data of integrated logging engineering monitoring, and to perform geological structure analysis based on the integrated logging engineering monitoring remote sensing data, thereby obtaining geological structure data; Surface lithology identification module: used to extract multispectral features of the integrated well logging project based on the integrated well logging project monitoring remote sensing data, thereby obtaining multispectral data of the integrated well logging project; and to identify surface lithology based on the multispectral data of the integrated well logging project, thereby obtaining surface lithology data. Oil and gas reservoir spatial distribution analysis module: used to construct a comprehensive three-dimensional model of the well logging project based on geological structure data and surface lithology data, thereby obtaining the comprehensive three-dimensional model of the well logging project; and to perform spatial distribution analysis of oil and gas reservoirs based on the comprehensive three-dimensional model of the well logging project, thereby obtaining spatial distribution data of oil and gas reservoirs; The reservoir stability analysis module is used to acquire oil and gas reservoir temperature sensing data and perform reservoir deformation analysis on the spatial distribution data of the oil and gas reservoir based on this data, thereby obtaining reservoir deformation data. It then performs reservoir stability analysis based on the deformation data, thereby obtaining reservoir stability data. Specifically, it acquires oil and gas reservoir temperature sensing data and plots a temperature gradient map based on this data, thereby obtaining an oil and gas reservoir temperature gradient map. Finally, it identifies high-temperature regions in the spatial distribution data of the oil and gas reservoir based on the temperature gradient map, thereby obtaining high-temperature oil and gas reservoir data. The process involves: obtaining spatial distribution data of oil and gas reservoirs; acquiring geological thermal expansion coefficients and baseline temperature distribution data of oil and gas reservoirs; calculating temperature variations in the spatial distribution data of high-temperature oil and gas reservoirs based on the baseline temperature distribution data to obtain oil and gas reservoir temperature variation data; calculating thermal strain based on the oil and gas reservoir temperature variation data and geological thermal expansion coefficients to obtain oil and gas reservoir thermal strain data; identifying stress concentration areas in the oil and gas reservoir thermal strain data to obtain reservoir deformation data; and conducting reservoir rock stability analysis based on the reservoir deformation data to obtain reservoir rock stability data. The integrated logging 3D model path optimization module is used to design drilling paths based on reservoir stability data to obtain drilling path data. It then optimizes the path of the integrated logging engineering 3D model based on the drilling path data, resulting in an optimized 3D model of the integrated logging engineering, which is then uploaded to the integrated logging engineering management platform to execute the model optimization task. The path optimization based on the drilling path data includes: using the 3D model and reservoir stability data for drilling path design, and employing a path optimization algorithm to plan the shortest path. In the shortest path calculation, the inclination angle, azimuth angle, and maximum horizontal stress direction parameters are combined to set the drilling dip angle range and maximum offset distance. The optimized path is then verified and iterated on the 3D model using a path correction tool.

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