Grid model generation method for fluid displacement process of well cementation engineering

Abstract

The invention relates to the technical field of numerical simulation, in particular to a grid model generation method for the fluid displacement process of well cementation engineering. The method comprises the following steps: acquiring geological data of a well cementation project area, and constructing an initial geological model; well cementation engineering design data are obtained, well cementation engineering topological structure modeling is carried out, and a well cementation engineering topological structure model is obtained; according to the initial geologic model and the well cementation engineering topological structure model, fluid dynamic simulation is carried out, and a fluid characteristic matrix is obtained; performing fluid displacement process simulation according to the fluid characteristic matrix to obtain fluid displacement process data; performing fluid displacement process optimization on the well cementation engineering design data based on the fluid displacement process data to obtain optimized fluid displacement process data, and performing resource allocation redistribution on the well cementation engineering design data according to the optimized fluid displacement process data to obtain optimized well cementation engineering design data. According to the invention, the accuracy of fluid displacement simulation can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of numerical simulation, and in particular to a grid model generation method for a fluid displacement process in a cementing engineering. Background Art

[0002] In the oil and gas industry, cementing is an important step to ensure the structural integrity of oil and gas wells and improve their production efficiency. The fluid displacement process is a key link in cementing engineering, and its main purpose is to replace the old fluid with new cement slurry in the wellbore to achieve good cementing quality. However, the traditional fluid displacement method has many defects in practical applications, resulting in unsatisfactory cementing effects and even affecting subsequent oil and gas production.

[0003] Initial cementing technology mainly relied on simple cement slurry and gravity flow principles. In recent years, the development of computer technology has provided new possibilities for numerical simulation of cementing engineering. As an important part of numerical simulation, the grid model generation method can help engineers better understand and predict the flow behavior of fluids in the wellbore. However, when dealing with complex geological conditions and wellbore structures, traditional grid generation methods often face problems such as uneven grid division and waste of computing resources, resulting in insufficient simulation accuracy and efficiency. Traditional fluid displacement process simulations mostly rely on numerical calculation methods based on uniform grids. These methods usually assume that the geometry of the wellbore is regular and the fluid properties are uniform. However, the actual situation is often more complicated. For example, the wellbore may have an irregular shape due to geological factors, and the fluid properties may change due to factors such as temperature and pressure. This simplified assumption leads to serious limitations on the accuracy and reliability of traditional models. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a grid model generation method for the fluid displacement process of cementing engineering to solve at least one of the above technical problems.

[0005] To achieve the above object, a grid model generation method for a fluid displacement process in a cementing engineering comprises the following steps:

[0006] Step S1: Acquire regional geological data of the cementing project, and integrate the regional geological structure characteristics of the cementing project regional geological data to obtain regional geological structure characteristic data; construct an initial geological model based on the regional geological structure characteristic data;

[0007] Step S2: Acquire cementing engineering design data, and perform cementing engineering structural feature extraction on the cementing engineering design data, thereby obtaining cementing engineering structural data; perform cementing engineering topological structure modeling based on the cementing engineering structural data, thereby obtaining a cementing engineering topological structure model;

[0008] Step S3: integrating the wellbore geometric characteristics according to the initial geological model and the cementing engineering topological structure model to obtain wellbore geometric characteristic data, and performing fluid dynamics simulation based on the regional geological structure characteristic data and the wellbore geometric characteristic data to obtain a fluid characteristic matrix;

[0009] Step S4: performing triangulation dynamic meshing according to the fluid characteristic matrix to obtain an adaptive fluid mesh model, and performing fluid displacement process simulation according to the adaptive fluid mesh model and cementing engineering structure data to obtain fluid displacement process data;

[0010] Step S5: optimizing the fluid displacement process of the cementing engineering design data based on the fluid displacement process data, thereby obtaining optimized fluid displacement process data, and reallocating the resource configuration of the cementing engineering design data according to the optimized fluid displacement process data, thereby obtaining optimized cementing engineering design data.

[0011] The present invention can obtain a more accurate initial geological model by integrating regional geological structure feature data. This model provides a solid foundation for subsequent simulation. An accurate geological model can better reflect the actual geological conditions and reduce the risk of cementing failure caused by geological factors. Detailed geological feature analysis provides a basis for cementing engineering design and helps engineers formulate cementing plans that adapt to specific geological conditions. By extracting the structural features of cementing engineering, complex engineering design information can be structured, making subsequent model establishment and analysis more efficient. The generated cementing engineering topological structure model can effectively reflect the geometric characteristics of the wellbore and its relationship with the surrounding geology, and promote the understanding of fluid flow behavior. By integrating the wellbore geometric feature data, the flow of fluid in the wellbore can be more accurately simulated, and the reliability of fluid dynamics simulation can be improved. Combining regional geological features and wellbore geometric data can help create a fluid property matrix that is more adapted to actual conditions and improve the accuracy of the model. Through triangulation dynamic mesh division, it can effectively cope with complex fluid flow conditions and significantly improve calculation efficiency and simulation accuracy. Using an adaptive fluid grid model to simulate the fluid displacement process can accurately predict the flow and mixing of fluids, thereby optimizing the cementing effect. Based on the analysis of fluid displacement process data, it is possible to identify and optimize the fluid displacement process, reduce resource waste, and improve overall efficiency. Through the analysis of the optimization process, reasonable resource allocation and redistribution can help reduce costs and improve the economy and feasibility of the project. In summary, through the implementation of the above steps, cementing projects can not only fully consider the complex geological and fluid characteristics in the design stage, but also significantly improve the effect of fluid displacement through refined dynamic meshing and simulation analysis. These improvements will help improve the overall quality of cementing and the efficiency of subsequent oil and gas production, and ultimately achieve higher economic benefits and safety.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: Acquire the regional geological data of the cementing project, and divide the regional geological data of the cementing project into geological characteristics, so as to obtain regional fluid characteristic data and regional soil characteristic data;

[0014] Step S12: evaluating regional soil permeability according to regional soil characteristic data, thereby obtaining regional soil permeability data;

[0015] Step S13: performing regional fluid circulation analysis on the regional fluid characteristic data according to the regional soil permeability data, thereby obtaining regional fluid circulation data;

[0016] Step S14: integrating regional geological structure characteristics based on regional soil permeability data and regional fluid circulation data, thereby obtaining regional geological structure characteristic data;

[0017] Step S15: construct an initial geological model based on regional geological structure characteristic data.

[0018] The present invention systematically collects geological data in the region, including rock and soil properties, structural features, etc., to provide basic information for subsequent analysis. Through the division of geological features, the characteristics of different strata and the potential fluid distribution can be identified, which provides a basis for engineering design. Evaluating soil permeability can help engineers understand the water flow characteristics of the soil and evaluate its ability to resist leakage and retain moisture. Through permeability data, designers can optimize the selection of cementing materials and construction methods to ensure the cementing effect. By analyzing fluid mobility, the flow characteristics of the fluid in the soil can be predicted, and the potential fluid migration path and speed can be understood. Identifying areas where fluid mobility changes helps to assess possible environmental risks and formulate corresponding countermeasures. Integrating soil permeability and mobility data helps to fully understand the geological structure of the region and identify complex underground environments. By integrating multiple feature data, the accuracy and reliability of subsequent geological models can be improved, providing more powerful support for engineering decisions. The initial geological model is an important basis for subsequent engineering design, construction and monitoring, and can provide engineers with visual geological information. The model can help the engineering team evaluate the changes in geological conditions in real time during the construction process, optimize the construction plan, and reduce engineering risks.

[0019] Optionally, step S12 is specifically:

[0020] Step S121: regional soil characteristic data is divided into regional grids, thereby obtaining grid soil characteristic data;

[0021] Step S122: extracting soil particle size characteristics and soil porosity characteristics from the grid soil characteristic data, thereby obtaining grid soil particle size data and grid porosity data;

[0022] Step S123: performing particle size grid distribution statistics according to the grid soil particle size data, thereby obtaining dense grid data of large soil particle sizes and dense grid data of small soil particle sizes;

[0023] Step S124: performing porosity grid classification based on the grid porosity data, thereby obtaining high soil porosity grid data and low soil porosity grid data;

[0024] Step S125: performing a grid area intersection operation on the dense grid data of large soil particles and the grid data of high soil porosity, thereby obtaining the regional data of high soil permeability; performing a grid area intersection operation on the dense grid data of small soil particles and the grid data of low soil porosity, thereby obtaining the regional data of low soil permeability;

[0025] Step S126: calculating soil permeability for the high soil permeability area data and the low soil permeability area data respectively, thereby obtaining permeability values ​​for the high soil permeability area and the low soil permeability area;

[0026] Step S127: Merging regional spatial data according to the permeability values ​​of the high soil permeability area and the permeability values ​​of the low soil permeability area, thereby obtaining regional soil permeability data.

[0027] The present invention can convert continuous soil data into discrete grid data by gridding regional soil characteristic data, which is convenient for subsequent analysis and processing. This division makes the spatial distribution of soil characteristics clearer, and provides a basis for subsequent feature extraction and statistical analysis. By extracting the particle size characteristics and porosity characteristics of the soil, the physical properties of the soil can be deeply understood. By statistically analyzing the distribution of grid soil particle sizes, dense areas of large and small particle sizes in the soil can be identified. This helps to evaluate the physical structure and function of the soil, and thus provides important information for land use planning, plant growth and soil and water conservation. Classifying porosity helps to distinguish the permeability of different soil types to water and air. This process can better understand the hydrological characteristics of the soil. : Through intersection operations, high permeability and low permeability soil areas can be identified, which helps to determine which areas are suitable for fluid flow. Calculations are performed for different permeability areas to quantify the water permeability of each area. The data of high and low soil permeability areas are merged to form overall regional soil permeability data.

[0028] Optionally, step S13 is specifically:

[0029] Step S131: integrating the regional fluid characteristic data with the spatial distribution of fluid soil to obtain regional fluid distribution data, and dividing the regional fluid distribution data into regions to obtain fluid dense distribution region data and fluid sparse distribution region data;

[0030] Step S132: performing regional soil fluid dynamics simulation according to the regional soil permeability data and the regional fluid characteristic data, thereby obtaining regional soil fluid simulation data;

[0031] Step S133: Calculating the fluid flow velocity in the permeability region based on the regional soil fluid simulation data, thereby obtaining the fluid flow velocity data in the permeability region, and dividing the fluid flow velocity data in the permeability region into regions, thereby obtaining the fluid high flow velocity region data and the fluid low flow velocity region data;

[0032] Step S134: performing a regional intersection operation on the fluid dense distribution area data and the fluid high flow rate area data, so as to obtain the high fluid flow area data; performing a regional intersection operation on the fluid sparse distribution area data and the fluid low flow rate area data, so as to obtain the low fluid flow area data;

[0033] Step S135: spatially merge the high fluid flowability region data and the low fluid flowability region data to obtain regional fluid flowability data.

[0034] The present invention can obtain detailed fluid distribution through the integration of regional fluid characteristic data, which helps to identify the dense and sparse distribution of fluids in different regions and support subsequent soil and fluid dynamics research. Such data integration provides a scientific basis for regional management and resource allocation. Dynamic simulation combined with soil permeability data can predict the movement behavior of fluids in the soil, provide key data for understanding the interaction between soil and fluids, and is of great significance for water resource management and pollution control. Through flow velocity calculation, not only can the movement speed of fluids in the soil be quantified, but also regional division can be carried out according to different flow velocities, which helps to optimize regional management measures. By spatially merging data of high-flowability and low-flowability areas, comprehensive regional fluid flowability data can be formed. This lays the foundation for a comprehensive assessment of regional fluid behavior and its environmental impact, enabling cementing project managers to formulate more reasonable measures.

[0035] Optionally, step S2 specifically includes:

[0036] Step S21: acquiring cementing engineering design data, and extracting cementing engineering structural features from the cementing engineering design data, thereby obtaining cementing engineering structural data;

[0037] Step S22: classifying the cementing engineering structure data into engineering layer structures, thereby obtaining cementing layer structure data and connection layer structure data;

[0038] Step S23: integrating the spatial relationship of the cementing layer hierarchy based on the cementing layer structure data, thereby obtaining the cementing layer hierarchy structure data;

[0039] Step S24: performing cementing hierarchical connection relationship analysis on the cementing layer hierarchical structure data and the connection layer structure data, thereby obtaining cementing hierarchical structure connection data;

[0040] Step S25: performing cementing engineering topological structure modeling based on the cementing hierarchical structure connection data, thereby obtaining a cementing engineering topological structure model.

[0041] The present invention can provide accurate basic information for subsequent analysis and processing by collecting detailed cementing engineering design data. This process ensures the comprehensiveness and accuracy of the data and avoids subsequent errors caused by missing data. Feature extraction and classification of cementing engineering structure data can clearly distinguish the characteristics of cementing layers and connection layers. This classification is helpful for subsequent analysis, so that engineers can formulate corresponding design and construction plans for different types of layer structures, thereby improving the pertinence and effectiveness of construction. Integrating the hierarchical spatial relationship of cementing layers helps to build a connection and interaction model between layers. This process can reveal the spatial layout and mutual influence between different cementing layers, providing an important basis for subsequent structural analysis and optimization. By analyzing the connection relationship between cementing levels, key connection points and potential problems in the hierarchical structure can be identified. This analysis helps engineers understand the interaction between layers and provides important data support for optimizing design and improving the overall performance of cementing engineering. Based on the obtained data, a topological structure model of cementing engineering is established, which can better visualize and understand the overall structure of the engineering. This model provides a powerful tool for subsequent simulation, analysis and decision-making, allowing engineers to perform design optimization and risk assessment more efficiently.

[0042] Optionally, step S24 is specifically:

[0043] Step S241: extracting structural features of the cementing layer hierarchical structure data and the connection layer structure data respectively, thereby obtaining hierarchical structure feature data and connection structure feature data;

[0044] Step S242: Calculating the structural similarity of the hierarchical structure feature data and the connection structure feature data, thereby obtaining structural similarity data;

[0045] Step S243: clustering the cementing layer hierarchical structure data and the connection layer structure data according to the structure similarity data, thereby obtaining connection layer-cementing layer clustering data;

[0046] Step S244: Integrate the cementing hierarchical connection relationship based on the connection layer-cementing layer clustering data, so as to obtain cementing hierarchical structure connection data.

[0047] The present invention can filter out key features, reduce data redundancy, and make subsequent analysis more efficient by extracting the hierarchical structure of the cementing layer and the structural features of the connection layer. Obtaining the hierarchical structure features and the connection structure features helps to fully understand the geological conditions and technical requirements of cementing, and provides a basis for subsequent similarity calculations. Calculating the similarity between different hierarchical structures and connection layers enables cementing cases with similar geological conditions and engineering requirements to be identified. By quantifying the similarity, data support is provided for engineers to select appropriate cementing technologies and methods, thereby improving the scientific nature of decision-making. Cluster analysis can discover potential structural patterns, thereby effectively classifying different types of cementing layers and connection layers, which is helpful for engineering management. Through the clustering results, resources and technical support can be better allocated, and personalized solutions can be designed for specific types of structures. Integrating the connection relationship of the cementing hierarchy based on the clustering results can reduce structural redundancy and improve the coherence and stability of the overall structure. By optimizing the connection relationship, the pressure resistance and leakproof performance of cementing can be improved, and the overall safety and reliability of the project can be enhanced.

[0048] Optionally, step S3 specifically includes:

[0049] Step S31: integrating the wellbore geometric features based on the initial geological model and the cementing engineering topological structure model, thereby obtaining wellbore geometric feature data;

[0050] Step S32: Performing wellbore geometry visualization modeling according to the wellbore geometry feature data, thereby obtaining a wellbore geometry model;

[0051] Step S33: describing the relative position characteristics of the wellbore and the formation based on the wellbore geometric model, thereby obtaining the relative position data of the wellbore and the formation;

[0052] Step S34: extracting regional stratigraphic structure characteristics from the regional geological structure characteristic data, thereby obtaining regional stratigraphic structure data;

[0053] Step S35: constructing a regional fluid activity model based on the regional stratum structure data and the wellbore geometry model according to the wellbore-formation relative position data, thereby obtaining a regional fluid activity model;

[0054] Step S36: performing fluid dynamics simulation according to the regional fluid activity model, thereby obtaining displacement fluid dynamics simulation data;

[0055] Step S37: constructing a fluid property matrix based on the displacement fluid dynamics simulation data.

[0056] The present invention can accurately extract the geometric feature data of the wellbore by integrating the initial geological model and the cementing engineering topological structure model. This provides a basis for subsequent modeling, ensures the authenticity and reliability of the model, and helps to avoid engineering errors caused by inaccurate geometric features. Visual modeling can intuitively present complex wellbore geometric features, making it easier for engineers and decision makers to understand the wellbore structure. This intuitive representation method helps to carry out more effective design and optimization and improve the operability of the project. Describing the relative position characteristics of the wellbore and the surrounding strata can provide important spatial relationship data for the subsequent analysis of fluid behavior, which helps to identify potential fluid flow channels and optimize mining strategies. Extracting regional stratigraphic structural feature data provides a deeper perspective for understanding the geological environment, which helps to identify geological heterogeneity in the region, improve the accuracy of oil and gas reservoir evaluation, and support scientific decision-making. Combining the wellbore-stratum relative position data and the regional stratigraphic structure data, constructing a regional fluid activity model enables a more comprehensive understanding of the movement and distribution of fluids in the strata, which helps to evaluate the exploitability of fluid resources and optimize the well layout. Fluid dynamics simulation based on the regional fluid activity model can predict the behavior of fluids in the wellbore and surrounding strata. The dynamic data obtained through simulation can identify possible problems in advance, such as uneven fluid flow, so that preventive measures can be taken. The establishment of a fluid property matrix provides a rich data basis for subsequent fluid analysis and optimization. This matrix can help engineers evaluate the production efficiency under different fluid conditions and support more scientific decision-making.

[0057] Optionally, step S4 is specifically:

[0058] Step S41: selecting regional grid density according to the fluid characteristic matrix, thereby obtaining regional grid density data;

[0059] Step S42: triangulating and meshing the regional fluid activity model based on the regional mesh density data, thereby obtaining an adaptive fluid mesh;

[0060] Step S43: extracting the dynamic change characteristics of the fluid state according to the fluid characteristic matrix, thereby obtaining the fluid state dynamic data, and performing fluid variable grid interpolation on the adaptive fluid grid according to the fluid state dynamic data, thereby obtaining the adaptive fluid grid model;

[0061] Step S44: Perform fluid displacement process simulation according to the adaptive fluid grid model and cementing engineering structure data, so as to obtain fluid displacement process data.

[0062] The present invention can select appropriate grid density in different areas according to the properties of the fluid (such as viscosity, density, etc.) through the fluid property matrix. This selection avoids the use of too fine grids in unnecessary areas, thereby saving computing time and resources. Using a higher grid density in areas where the flow characteristics change greatly makes the simulation results more accurate and can better capture the complex behavior of fluid flow. The triangulation method allows the grid to freely adjust its shape and size in different areas to better adapt to complex geometric shapes and flow characteristics. According to the different characteristics of fluid flow, the grid structure is dynamically adjusted to balance the computational efficiency and the accuracy of the results. By extracting the dynamic change characteristics of the fluid state, the behavior changes of the fluid can be reflected in real time, providing important data support for subsequent simulations. Grid interpolation technology makes it possible to accurately calculate fluid variables on an adaptive grid, thereby improving the expressiveness and reliability of the model, especially when the flow state changes rapidly. Through the simulation of the fluid displacement process, the fluid behavior in cementing engineering can be predicted and evaluated, providing a scientific basis for design and implementation.

[0063] Optionally, step S41 is specifically:

[0064] According to the fluid property matrix, the fluid property change amount in the simulation area is counted, so as to obtain the data of the area with high fluid property change amount and the data of the area with low fluid property change amount;

[0065] The dense grid division density is selected for the data of the area with high fluid characteristic variation, so as to obtain the dense grid area division density data;

[0066] The sparse grid division density is selected for the data of the area with low fluid characteristic variation, so as to obtain the sparse grid area division density data;

[0067] The dense grid area division density data and the sparse grid area division density data are merged to obtain the regional grid density data.

[0068] The present invention can accurately identify high and low change areas by statistically analyzing the change in fluid properties in the simulation area, which is of great significance for the research and application of fluid mechanics. This can help allocate computing resources and concentrate on in-depth analysis in high-change areas, thereby improving simulation efficiency. Dense grid division is used in areas with large changes in fluid properties, more accurate flow field distribution information can be obtained, the accuracy of numerical solutions can be improved, and small features and complex flow patterns in the flow, such as vortices, boundary layers, etc., can be captured, providing a more comprehensive flow analysis. Sparse grid division is used in areas with small changes, which can significantly reduce the amount of calculation and reduce computing costs. By simplifying the calculation process, the overall simulation speed can be accelerated, making the solution of large-scale fluid dynamics problems more efficient. The data of dense and sparse grids can be merged to form unified regional grid density data, providing comprehensive information support for subsequent numerical analysis. By integrating data of different densities, the overall trend of fluid property changes can be more clearly displayed, which is helpful for the interpretation and application of results.

[0069] Optionally, step S44 is specifically:

[0070] Extract displacement fluid characteristics from cementing engineering design data to obtain designed displacement fluid data;

[0071] The fluid flow boundary conditions are set based on the cementing engineering structure data and the designed displacement fluid data, thereby obtaining the fluid flow boundary conditions;

[0072] Selecting simulation time parameters according to the fluid state dynamic data, thereby obtaining a simulation time parameter group;

[0073] The fluid displacement process is simulated according to the adaptive fluid grid model, fluid flow boundary conditions and simulation time parameter group, so as to obtain the fluid displacement process data.

[0074] The present invention can select a displacement fluid that is more suitable for specific cementing conditions and improve the cementing effect by accurately extracting fluid properties. Reasonable selection of displacement fluid can reduce material costs and improve construction efficiency. Understanding fluid properties helps to avoid well wall rupture or other safety hazards caused by improper fluid selection. Reasonable boundary conditions can more realistically reflect the flow state of the fluid under actual conditions. By accurately setting the flow conditions, the fluid behavior can be better predicted and the engineering risk can be reduced. Reasonable selection of simulation time parameters can reduce the consumption of computing resources while ensuring the accuracy of the results. According to the changes in the fluid state, the simulation time can be flexibly adjusted to ensure real-time feedback and adaptability. The simulation can provide dynamic data such as pressure changes and flow velocity during the fluid displacement process, which is helpful for in-depth analysis of the displacement process. Through the simulation results, the cementing design can be evaluated and optimized to ensure better cementing quality and construction efficiency. The simulation results provide data support for subsequent decision-making and help engineers formulate more reasonable construction plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0076] Figure 1 It is a schematic diagram of the steps of the grid model generation method for the fluid displacement process of cementing engineering according to the present invention;

[0077] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0078] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0079] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0080] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0082] 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 only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0083] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for generating a grid model for a fluid displacement process in a cementing engineering, the method comprising the following steps:

[0084] Step S1: Acquire regional geological data of the cementing project, and integrate the regional geological structure characteristics of the cementing project regional geological data to obtain regional geological structure characteristic data; construct an initial geological model based on the regional geological structure characteristic data;

[0085] In the present embodiment, geological data of the cementing engineering area are collected through field exploration, drilling and existing geological survey reports. These data include stratum distribution, lithology changes, porosity, permeability, etc. The stratum data can be obtained through logging data, seismic reflection data and core analysis. The geological data are integrated to analyze the distribution law of different strata and identify the geological structures such as major faults and folds in the region. Various geological features (such as structural units, rock layer thickness changes, stress fields, etc.) are integrated into a unified geological model through numerical simulation or geological mapping software (such as Gocad, Petrel, etc.). Based on the integrated regional geological structure characteristic data, an initial three-dimensional geological model is constructed. The input data of the model include stratum thickness, fault information, stratum hardness, etc., which are simulated by numerical modeling tools to ensure that the model can reflect the actual geological conditions. The details considered in the model include geological stratum distribution, pore structure and permeability differences, etc.

[0086] Step S2: Acquire cementing engineering design data, and perform cementing engineering structural feature extraction on the cementing engineering design data, thereby obtaining cementing engineering structural data; perform cementing engineering topological structure modeling based on the cementing engineering structural data, thereby obtaining a cementing engineering topological structure model;

[0087] In this embodiment, design data is obtained from the cementing design stage, including wellbore design, cementing material selection, cementing pressure requirements, pumping rate, fluid properties, etc. These data can be obtained through design engineering software (such as Wellplan, Schlumberger's Design Studio, etc.). The wellbore structural characteristics (such as well depth, well diameter change, cementing link design, etc.) are extracted from the design data, and other important parameters (such as packer, casing and rock formation contact surface, etc.) are obtained according to the cementing process requirements. Based on the cementing design data, a topological structure model of the wellbore is established. The model should reflect the geometric shape of the wellbore, the connection relationship between each link, and the changes at different levels during the cementing process. Use topological modeling tools (such as AutoCAD, OpenFlow, etc.) to construct a network topology structure containing elements such as casing and annulus.

[0088] Step S3: integrating the wellbore geometric characteristics according to the initial geological model and the cementing engineering topological structure model to obtain wellbore geometric characteristic data, and performing fluid dynamics simulation based on the regional geological structure characteristic data and the wellbore geometric characteristic data to obtain a fluid characteristic matrix;

[0089] In this embodiment, the geometric features of the wellbore (such as the curvature of the wellbore, diameter change, wellbore design, etc.) are extracted by combining the regional geological model and the cementing engineering topological structure model. Computer-aided design (CAD) tools and three-dimensional modeling software are used to integrate the geometric data of the wellbore into the geological model to ensure that the geological model is consistent with the cementing design parameters. Fluid flow simulation is performed based on the integrated wellbore geometric data and regional geological structure data. CFD (computational fluid dynamics) software (such as ANSYS Fluent, COMSOL, etc.) is used to simulate the dynamic behavior of the fluid during the cementing process. The input parameters include the viscosity, density, flow rate, etc. of the cementing fluid, as well as the geometry and geological conditions of the wellbore. According to the results of the fluid dynamics simulation, the motion trajectory, pressure changes, etc. of the fluid in the wellbore are calculated. The fluid property matrix will include important characteristics such as fluid flow rate, pressure distribution, temperature changes, etc. in different regions to support subsequent simulation and optimization processes.

[0090] Step S4: performing triangulation dynamic meshing according to the fluid characteristic matrix to obtain an adaptive fluid mesh model, and performing fluid displacement process simulation according to the adaptive fluid mesh model and cementing engineering structure data to obtain fluid displacement process data;

[0091] In this embodiment, fluid grid division is performed based on the fluid property matrix. Dynamic grid division is performed using an adaptive grid generation algorithm (such as Delaunay triangulation), and the grid density is automatically adjusted according to the fluid flow characteristics. Locally refine the grid according to the flow velocity changes and pressure gradients in different areas such as the wellbore, annulus, and formation to improve the calculation accuracy. An adaptive fluid grid model is generated through dynamic grid division to ensure appropriate resolution in different flow areas. The grid division should adapt to the velocity and pressure changes of the fluid flow, and the refined grid will provide higher calculation accuracy. Based on the adaptive fluid grid model, the fluid displacement process is simulated in combination with the cementing structure data. Multiphase fluid simulation is performed using CFD software to simulate the exchange process between cementing fluid and the original underground fluid, analyze the efficiency and effect of fluid displacement, and obtain fluid displacement process data. Changes in wellhead flow, pressure and other conditions are considered during the simulation process.

[0092] Step S5: optimizing the fluid displacement process of the cementing engineering design data based on the fluid displacement process data, thereby obtaining optimized fluid displacement process data, and reallocating the resource configuration of the cementing engineering design data according to the optimized fluid displacement process data, thereby obtaining optimized cementing engineering design data.

[0093] In this embodiment, according to the simulation results of the fluid displacement process, the bottleneck in the fluid flow is identified and the fluid displacement process is optimized. For example, the fluid displacement efficiency is improved by adjusting the pumping rate, fluid ratio, additive use, etc. The displacement process is multi-objective optimized using an optimization algorithm (such as a genetic algorithm, a particle swarm optimization, etc.) to improve the efficiency and effect of the cementing process. According to the optimized fluid displacement process, the cementing design data is updated, the cementing fluid ratio, pumping parameters, and pressure control are optimized, etc., to ensure that the flow of the fluid during the cementing process is smoother and reduce the mutual interference between the cementing fluid and the formation fluid. According to the optimized cementing process data, the resources used in the cementing project (such as personnel, equipment, fluid materials, etc.) are reallocated to ensure efficient use of resources. The resource optimization model can be used to optimize the scheduling of equipment, the procurement of materials, the allocation of personnel, etc., thereby improving the execution efficiency and cost-effectiveness of the entire cementing project.

[0094] Optionally, step S1 specifically includes:

[0095] Step S11: Acquire the regional geological data of the cementing project, and divide the regional geological data of the cementing project into geological characteristics, so as to obtain regional fluid characteristic data and regional soil characteristic data;

[0096] In this embodiment, the geological data of the cementing engineering area is obtained by drilling, geological exploration and remote sensing technology. The data includes stratum thickness, rock type, soil properties, groundwater level, seismic wave velocity, etc. For example, the geological reflection wave data of the underground layer is obtained by using a seismic exploration method (such as reflection seismic exploration), and then the geological stratum is determined. The core analysis during the drilling process is used to extract parameters such as rock type, porosity, permeability, etc. of different strata. The acquired data is analyzed and classified, and the region is divided into different geological units according to geological characteristics such as lithology, structure, stratum distribution, stress field, etc. For example, unsupervised learning is performed on geological data through a machine learning algorithm (such as K-means clustering), and the region is divided into different soil types (such as clay, sand, rock, etc.) according to the physical and chemical properties of the stratum. According to the geological characteristics, the fluid characteristics in different geological units are inferred, such as hydrological conditions, groundwater flow characteristics, oil and gas layer distribution, etc. For example, the groundwater layer has different fluid properties, such as water viscosity, salinity, etc. By analyzing the permeability and porosity of the geological layer, the underground fluid fluidity and fluid type are predicted. Based on information such as lithology and soil composition, soil parameters such as permeability, porosity and water retention capacity are further extracted. For example, sandy soils are generally more permeable, while clay soils are less permeable, which can be obtained through laboratory analysis or geological surveys.

[0097] Step S12: evaluating regional soil permeability according to regional soil characteristic data, thereby obtaining regional soil permeability data;

[0098] In this embodiment, laboratory permeability tests can be performed on different soil samples in the area to determine the permeability coefficient (K) of the soil. Common methods include conventional hydraulic tests, gas permeability tests, etc. For example, by using a conventional constant head permeability test, the permeability of different soil layers under different pressures is determined. Based on the permeability coefficient data of the soil samples obtained, combined with the regional geological characteristics, the overall permeability of the soil is evaluated. This can be modeled based on factors such as the particle composition, moisture content, and compaction of the soil using statistical methods such as multiple regression analysis. For example, existing soil permeability data (such as the permeability coefficients of sand, loam, clay, etc.) are used in combination with geological structures for prediction, thereby evaluating the soil permeability of different regions. Based on the evaluation results, a soil permeability distribution map of the region is generated, indicating the permeability range of each geological unit, and used as the basis for subsequent fluid flow analysis.

[0099] Step S13: performing regional fluid circulation analysis on the regional fluid characteristic data according to the regional soil permeability data, thereby obtaining regional fluid circulation data;

[0100] In this embodiment, a fluid dynamics model (such as Darcy's law) is combined with soil permeability data to analyze the flow characteristics of the fluid in the region. The fluid flowability is evaluated by factors such as the flow velocity, direction, and pressure change of the fluid in different soil layers. For example, the Darcy equation is used: Q = -K\cdot A\cdot\frac{\Delta P}{L}; where Q is the flow rate, K is the permeability, A is the cross-sectional area, ΔP is the pressure difference, and L is the flow distance to simulate the fluid flow in different geological units. According to the permeability of different soil layers in the region and the physical properties of the fluid (such as water, oil, natural gas, etc.), the flow behavior of the fluid is analyzed and the flow path of the underground fluid is predicted. For example, for high permeability areas, the fluid will spread rapidly, while low permeability areas will cause fluid accumulation or retention. Thus, a distribution map of fluid flowability in the region is obtained, which areas are clear as fluid-enriched areas or fluid retention areas, and decision support is provided for cementing projects.

[0101] Step S14: integrating regional geological structure characteristics based on regional soil permeability data and regional fluid circulation data, thereby obtaining regional geological structure characteristic data;

[0102] In this embodiment, in combination with soil permeability and fluid circulation data, geological modeling software (such as Petrel, GOCAD, etc.) is used to integrate regional geological features and construct a three-dimensional geological model. For example, based on known soil layer permeability and fluid circulation data, combined with different lithology, stratum distribution and structural characteristics in the region, a three-dimensional digital model reflecting the real geological structure is generated using finite element method or simulation technology. The integrated geological structure characteristic data is analyzed to identify geological anomalies in different regions, such as faults, folds and other structural features that may affect the quality of cementing. For example, by comparing the stress field, stratum thickness, and lithology distribution in different regions, possible cracks or other underground structures are predicted, thereby affecting the distribution and flow of fluids. According to the analysis results, the geological structure characteristic data of the region are generated, such as underground faults, crack distribution maps, the possibility of interlayer sliding, etc., to help avoid potential geological risks when designing cementing projects.

[0103] Step S15: construct an initial geological model based on regional geological structure characteristic data.

[0104] In the present embodiment, based on the integrated regional geological structure characteristic data, an initial geological model is constructed using a three-dimensional modeling software. The model should include the spatial distribution of underground rock formations, fluid properties, and potential geological structures such as faults and cracks. For example, using Petrel software, a detailed underground geological model is constructed by inputting the collected drilling, exploration and permeability data, and the model parameters are set according to the actual working conditions. The preliminary constructed geological model is verified, and the accuracy and reliability of the model are checked by comparing the field test data (such as drilling data, geological exploration data). For example, it is verified whether the permeability and fluid flow characteristics predicted by the model are consistent with the field observations, and if not, the model parameters are adjusted. The construction of the initial geological model is finally completed as the basic data for subsequent cementing engineering design, risk assessment and fluid monitoring.

[0105] Optionally, step S12 is specifically:

[0106] Step S121: regional soil characteristic data is divided into regional grids, thereby obtaining grid soil characteristic data;

[0107] In this embodiment, soil characteristic data are collected from the region, including soil particle size, porosity, humidity, pH value and other data. These data can be obtained through soil sensors, remote sensing image analysis, or soil sample collection and laboratory testing. Then, the area is divided into regular grids through GIS technology, for example, each grid is 10km x 10km, or the grid size is adjusted based on the distribution of soil sampling points. In the gridding process, the data of each collection point is mapped to the corresponding grid unit in the vertical and horizontal directions according to its spatial position to form the soil characteristic data of each grid. For example, the soil porosity data in a certain area is gridded through GIS so that each grid unit corresponds to a porosity value.

[0108] Step S122: extracting soil particle size characteristics and soil porosity characteristics from the grid soil characteristic data, thereby obtaining grid soil particle size data and grid porosity data;

[0109] In this embodiment, based on the obtained grid soil characteristic data, a statistical method is used to extract soil particle size characteristics (such as sand, silt, clay ratio) and porosity characteristics. The extraction of soil particle size can be carried out by analyzing the mass percentage of different particle sizes in the soil sample, and the particle size data is represented by the soil texture (such as sand, clay) of each grid. In the extraction of porosity, the porosity can be calculated by testing the dry weight to wet weight ratio of each soil sample in the laboratory, or the porosity value can be estimated according to the reflectance spectrum characteristics of the soil by remote sensing technology. The processed data will be stored as particle size distribution data and porosity data corresponding to the grid, which is convenient for subsequent analysis.

[0110] Step S123: performing particle size grid distribution statistics according to the grid soil particle size data, thereby obtaining dense grid data of large soil particle sizes and dense grid data of small soil particle sizes;

[0111] In this embodiment, the extracted soil particle size data is further statistically processed. First, a threshold value of the particle size is set. For example, the large particle size is defined as the part with a particle size greater than 2 mm, and clay is defined as the part with a particle size less than 0.002 mm. Then, based on the particle size data in the grid, the proportion of large particle size and small particle size in each grid is counted. If the proportion of large particle size in a grid exceeds 60%, the grid is a large particle size dense grid; if the proportion of small particle size exceeds 60%, the grid is a small particle size dense grid. This process can be completed by simple statistical analysis (such as ratio calculation and threshold determination). The grid data finally obtained can be the particle size distribution type corresponding to each grid (large particle size dense grid or small particle size dense grid).

[0112] Step S124: performing porosity grid classification based on the grid porosity data, thereby obtaining high soil porosity grid data and low soil porosity grid data;

[0113] In this embodiment, the porosity values ​​are classified according to the obtained grid porosity data. For example, grids with porosity values ​​greater than 30% are defined as high porosity grids, grids with porosity values ​​less than 10% are defined as low porosity grids, and the remaining grids are medium porosity grids. The classification method can be implemented by simple threshold segmentation, or by adaptive classification based on distribution characteristics through machine learning algorithms (such as clustering algorithms). The key to this process is to determine the appropriate porosity range and set appropriate classification standards through experimental data and geological knowledge.

[0114] Step S125: performing a grid area intersection operation on the dense grid data of large soil particles and the grid data of high soil porosity, thereby obtaining the regional data of high soil permeability; performing a grid area intersection operation on the dense grid data of small soil particles and the grid data of low soil porosity, thereby obtaining the regional data of low soil permeability;

[0115] In this embodiment, a GIS platform is used to perform spatial intersection operations. For dense grids with large soil particles and high porosity grids, the intersection of these two data sets is taken, and the resulting grid represents the high permeability area; similarly, for dense grids with small particles and low porosity grids, an intersection operation is performed, and the resulting grid represents the low permeability area. The specific steps of the intersection operation are: use the spatial query tool to perform spatial overlap analysis on the two data sets, and calculate the area where the characteristic conditions of the two grids are met. For example, the intersection function (Intersection) in QGIS or ArcGIS can be used to intersect two sets of grid data to obtain permeability area data.

[0116] Step S126: calculating soil permeability for the high soil permeability area data and the low soil permeability area data respectively, thereby obtaining permeability values ​​for the high soil permeability area and the low soil permeability area;

[0117] In this embodiment, the permeability of each grid is calculated using a hydrogeological model or experimental data. For example, the soil permeability in a high permeability area can be estimated using Darcy's law, where the permeability (K) is closely related to the soil's particle size, porosity and other characteristics. Experimentally, the permeability values ​​of different regions can be inferred by measuring the permeability rate of a specific soil sample through a flow experiment. Based on these experimental data and regional characteristics, the high permeability and low permeability areas are calculated separately, and finally the permeability value corresponding to each grid unit is obtained.

[0118] Step S127: Merging regional spatial data according to the permeability values ​​of the high soil permeability area and the permeability values ​​of the low soil permeability area, thereby obtaining regional soil permeability data.

[0119] In this embodiment, the permeability data of the high permeability area and the low permeability area are spatially merged according to the permeability values ​​obtained for each area. Through the GIS system, the permeability values ​​of all grids are inserted into a unified spatial data structure to achieve spatial visualization of the permeability data. In this process, an interpolation method (such as Kriging interpolation or inverse distance weighted interpolation) can be used to estimate the permeability values ​​of the unmeasured areas, thereby generating a soil permeability distribution map for the entire area.

[0120] Optionally, step S13 is specifically:

[0121] Step S131: integrating the regional fluid characteristic data with the spatial distribution of fluid soil to obtain regional fluid distribution data, and dividing the regional fluid distribution data into regions to obtain fluid dense distribution region data and fluid sparse distribution region data;

[0122] In the present embodiment, the fluid characteristic data of the soil in the region are collected and integrated, and these data are usually from geological exploration, remote sensing data, groundwater monitoring stations, soil testing experiments, etc. By using geographic information system (GIS) software, different fluid characteristic data (such as fluid concentration, flow direction, fluid type, etc.) are integrated into the same spatial framework to obtain a regional fluid distribution map. The data includes information such as the permeability, water retention, water content, and fluid flow rate of different soil layers. Subsequently, based on these data, the region can be divided, and the fluid dense area and the fluid sparse area are determined using cluster analysis methods (such as K-means algorithm or DBSCAN density clustering). Dense areas are areas with higher groundwater levels or stronger water flows, while sparse areas are areas with fewer water sources or slower fluid flows. Finally, by means of data visualization, a diagram of fluid distribution is obtained to assist subsequent steps in regional division.

[0123] Step S132: performing regional soil fluid dynamics simulation according to the regional soil permeability data and the regional fluid characteristic data, thereby obtaining regional soil fluid simulation data;

[0124] In this embodiment, soil fluid dynamics simulation is performed based on regional soil permeability data and regional fluid characteristic data. To achieve this, numerical simulation software (such as MODFLOW, HYDRUS-2D, etc.) can be used to simulate the flow process of water in the soil. First, the soil permeability data in the region (such as permeability based on experimental measurement or permeability model inferred by remote sensing data) is input. Combined with other physical properties of the soil (such as bulk density, porosity, soil type, etc.), fluid dynamics equations (such as Darcy's law and continuity equation) are used for calculation. Through this simulation process, the flow of fluid in the soil can be obtained, and the transfer, distribution and flow rate of fluid between different soil layers can be evaluated. The simulation data finally output includes information such as the moisture state, flow path and potential water flow of each soil layer.

[0125] Step S133: Calculating the fluid flow velocity in the permeability region based on the regional soil fluid simulation data, thereby obtaining the fluid flow velocity data in the permeability region, and dividing the fluid flow velocity data in the permeability region into regions, thereby obtaining the fluid high flow velocity region data and the fluid low flow velocity region data;

[0126] In this embodiment, the fluid flow rate of the permeability area is calculated based on the regional soil fluid simulation data previously obtained. The flow rate calculation is usually performed by the following formula: Q = K\cdot A\cdot\frac{dh}{dl}, where Q is the flow rate, K is the permeability, A is the flow cross-sectional area, and \frac{dh}{dl} is the head gradient (i.e., the slope of the water flow). According to the permeability of the soil in different regions (obtained through experimental or simulation data) and the fluid characteristics, the flow rate data of each region is calculated step by step. Numerical simulation methods (such as finite difference method and finite element method) can be used to accurately calculate the flow rate, and the results are spatially divided (for example, spatial interpolation is performed through GIS) to obtain data of the flow rate area. Finally, the regional flow rate data is divided into high flow rate areas and low flow rate areas to help determine the effectiveness of the water flow channel and the possible water resource distribution pattern.

[0127] Step S134: performing a regional intersection operation on the fluid dense distribution area data and the fluid high flow rate area data, so as to obtain the high fluid flow area data; performing a regional intersection operation on the fluid sparse distribution area data and the fluid low flow rate area data, so as to obtain the low fluid flow area data;

[0128] In this embodiment, the fluid dense distribution area and the fluid high flow velocity area are intersected to obtain the data of the high fluid circulation area; at the same time, the fluid sparse distribution area and the fluid low flow velocity area are intersected to obtain the data of the low fluid circulation area. This process usually relies on GIS software and spatial analysis tools. In GIS, by overlaying layers (such as fluid distribution maps and flow velocity maps), spatial query and overlay analysis functions are used to find the intersection area of ​​fluid dense areas and high flow velocity areas, as well as the intersection area of ​​fluid sparse areas and low flow velocity areas. For high circulation areas, it will appear as underground water channels with strong water flow capacity, while low circulation areas represent soil areas where water flow is blocked. In this way, more accurate water circulation zoning information is obtained to provide support for subsequent land use, resource management and other decisions.

[0129] Step S135: spatially merge the high fluid flowability region data and the low fluid flowability region data to obtain regional fluid flowability data.

[0130] In this embodiment, the obtained high fluid circulation area data and low fluid circulation area data are spatially merged to obtain the final regional fluid circulation data. This merging operation usually involves the merging of different data layers in GIS. A new circulation data set can be obtained by merging the raster data of the two (for example, by raster reclassification, resampling or pixel fusion). This data set can intuitively display the changes in fluid circulation in the region. The merged data can help analyze the fluid dynamics of the entire region and identify fluid channels and their possible change trends.

[0131] Optionally, step S2 specifically includes:

[0132] Step S21: acquiring cementing engineering design data, and extracting cementing engineering structural features from the cementing engineering design data, thereby obtaining cementing engineering structural data;

[0133] In this embodiment, during the cementing engineering design phase, the designer will use CAD software (such as AutoCAD) or a specialized oil and gas exploration and production design tool (such as Petrel or OpenWells) to draw cementing engineering drawings and engineering design plans. These design data include relevant information such as wellbore, casing, cementing mud, cementing density, wellhead elevation, etc. Through the data interface or API, these design data are exported from the design software, and the data is cleaned and formatted to extract the basic structural features of the cementing engineering, such as wellbore depth, casing material and thickness, cementing process parameters (such as cement slurry density, gelling properties, pressure requirements), etc. These data are stored as cementing engineering structural data to facilitate subsequent analysis and calculation.

[0134] Step S22: classifying the cementing engineering structure data into engineering layer structures, thereby obtaining cementing layer structure data and connection layer structure data;

[0135] In this embodiment, after obtaining the cementing engineering structure data, when classifying the layer structure, it is first necessary to divide the well section according to the depth of the well and the design requirements. By analyzing the well section data, different cementing layers (such as the surface layer, production layer and isolation layer) are identified, and the depth range of each cementing layer and its connection relationship with other layers are marked. For each layer, its cementing design parameters are extracted, including the type of casing, cementing pressure, cementing fluid formula, etc. At the same time, the connecting layer between different cementing layers (the connecting layer is usually an isolation layer or a transition layer) is extracted separately and saved as the connecting layer structure data.

[0136] Step S23: integrating the spatial relationship of the cementing layer hierarchy based on the cementing layer structure data, thereby obtaining the cementing layer hierarchy structure data;

[0137] In this embodiment, when integrating hierarchical spatial relationships based on cementing layer structural data, it is first necessary to clarify the physical relationship between cementing layers, such as the upper and lower positions of each layer, the relative distance between layers, the relative position of casings, etc. The integration of these spatial relationships can be achieved by integrating geological exploration data with wellbore data, and using three-dimensional modeling software to virtually reconstruct the wellbore to show the spatial position and structural relationship of each cementing layer. For example, the relative positions of cementing layers and casings are marked using CAD or 3D modeling tools, and compared with existing stratigraphic structure maps to ensure that the spatial structure of each cementing layer is reasonable. After data integration, the generated cementing layer hierarchical structure data can be presented in the form of graphics or charts, and saved in a standardized data format for subsequent analysis.

[0138] Step S24: performing cementing hierarchical connection relationship analysis on the cementing layer hierarchical structure data and the connection layer structure data, thereby obtaining cementing hierarchical structure connection data;

[0139] In this embodiment, in the cementing hierarchical connection relationship analysis, it is necessary to construct the connection relationship between cementing hierarchies by analyzing the interaction between each cementing layer and its upper and lower layers and connecting layers. First, by analyzing factors such as cementing fluid injection pressure and flow path, it is determined whether there is a direct connection relationship between cementing layers, or whether they are connected through certain transition layers (such as isolation layers or isolation layers). For example, for oil and gas wells, special attention should be paid to the connection between the isolation layer and the production layer. Algorithms are used to analyze these hierarchical connection data, and data mining technology is used to discover potential connection problems, such as casing shedding and incomplete cementing. Through the analysis of the connection relationship, it can be clarified which layers in the cementing structure are core structural layers and which layers are auxiliary layers or connecting layers, and cementing hierarchical structure connection data is generated and stored in a format supported by a database or other analysis tools for subsequent optimization and predictive analysis.

[0140] Step S25: performing cementing engineering topological structure modeling based on the cementing hierarchical structure connection data, thereby obtaining a cementing engineering topological structure model.

[0141] In this embodiment, when modeling the topological structure of the cementing project based on the cementing hierarchical structure connection data, the topological relationship between each cementing layer and the connection layer must be considered first. This can be achieved through the graph structure modeling method in graph theory, where each cementing layer is regarded as a node in the graph and the connection relationship between cementing layers is regarded as the edge between nodes. The topological structure of the cementing project is represented by constructing a graph consisting of nodes and edges. Furthermore, the topological structure model needs to be verified by numerical calculation and simulation software (such as ANSYS, COMSOL) to ensure the accuracy and reliability of the model. Specifically, the model will show the mechanical behavior of each cementing layer, the fluid flow path, and the interaction between the layers, providing a theoretical basis for subsequent engineering optimization, pressure analysis, risk assessment, etc. Finally, the topological structure model will be output as a digital file that meets the requirements of engineering applications, such as standard formats such as STL and STEP, for simulation, testing and further optimization in actual engineering.

[0142] Optionally, step S24 is specifically:

[0143] Step S241: extracting structural features of the cementing layer hierarchical structure data and the connection layer structure data respectively, thereby obtaining hierarchical structure feature data and connection structure feature data;

[0144] In this embodiment, it is necessary to analyze the data of the cementing layer and the connecting layer in detail to extract their structural features. The hierarchical structure data of the cementing layer generally includes the depth, thickness, geological attributes, porosity, permeability, etc. of the layer, while the structural data of the connecting layer includes the connection mode between different cementing layers, fluid transmission channels, material exchange conditions, etc. These data can be obtained through seismic exploration, drilling data or geological modeling software. For example, for the hierarchical structure data of the cementing layer, the geological attributes and thickness of each layer can be extracted as features, specifically including indicators such as the lithology type, porosity, and permeability of each layer. The structural data extraction of the connecting layer can be based on the fractures and cracks between different layers and the continuity of the fluid path, and the density changes and the distribution of cracks between each connecting layer can be obtained by means of CT scanning, geological survey, etc. Through these methods, the hierarchical structural feature data of the cementing layer obtained can include the properties of each cementing layer and its relative position in the geological column; the structural feature data of the connecting layer includes the material exchange characteristics between different layers and the continuity of the connecting channel, etc.

[0145] Step S242: Calculating the structural similarity of the hierarchical structure feature data and the connection structure feature data, thereby obtaining structural similarity data;

[0146] In this embodiment, structural similarity data is obtained by calculating the similarity between the cementing layer hierarchy and the connection layer structure. Similarity calculation can be based on multiple algorithms, such as Euclidean distance, cosine similarity, Manhattan distance, etc., to measure the similarity between two hierarchical structures or connection structures in the feature space. Assume that there are two cementing layers, and their feature data are A and B, respectively. Each feature data contains information such as lithology, porosity, and permeability. By vectorizing A and B and calculating the cosine similarity between them, the structural similarity values ​​of the two cementing layers can be obtained. If the structural similarity value is close to 1, it means that the two cementing layers are very similar in features; if the similarity value is close to 0, it means that the two cementing layers are very different in structure. In specific implementation, the similarity calculation algorithm is used to compare the features of each layer to generate a similarity matrix between the hierarchical structure features and the connection structure features. This similarity matrix provides basic data for subsequent clustering analysis.

[0147] Step S243: clustering the cementing layer hierarchical structure data and the connection layer structure data according to the structure similarity data, thereby obtaining connection layer-cementing layer clustering data;

[0148] In this embodiment, a clustering algorithm is used to perform cluster analysis on the cementing layer hierarchy and connection layer structure data based on the calculated structural similarity data. Common clustering methods include K-means clustering, hierarchical clustering (such as agglomerative clustering), DBSCAN, etc. Assuming that the similarity matrix between each cementing layer and the connection layer has been calculated, the K-means clustering algorithm can be used to cluster these hierarchical structure data. The K-means algorithm will assign hierarchical data to different clusters based on the similarity values ​​between layers. For example, assume that the cementing layer data is divided into three clusters by K-means clustering, and each cluster represents a type of cementing layer with similar structure; similarly, the structural data of the connection layer is also clustered in a similar way. The clustering result assigns a category identifier to each cementing layer and its corresponding connection layer, forming cluster data between the connection layer and the cementing layer. In this way, it can be clearly understood which connection layers have a strong correlation with which cementing layers.

[0149] Step S244: Integrate the cementing hierarchical connection relationship based on the connection layer-cementing layer clustering data, so as to obtain cementing hierarchical structure connection data.

[0150] In this embodiment, the connection of the cementing hierarchical structure is integrated based on the clustering data of the connection layer and the cementing layer obtained in the previous step. The core goal of this step is to establish the connection relationship between different cementing layers and optimize the connection structure of the cementing layer through clustering information. First, according to the clustering data, the clustering category of each cementing layer is matched with the clustering category of the connection layer to establish the hierarchical relationship between the cementing layer and the connection layer. For example, under a specific clustering category, all cementing layers may be associated with certain specific connection layers. Then, based on these associations, the connection relationship of the cementing hierarchy is integrated through an optimization algorithm (such as a graph optimization algorithm, a minimum spanning tree algorithm, etc.). For example, assuming that in a certain cementing layer clustering category, cementing layers A, B, and C are closely associated with connection layers 1, 2, and 3, then the fluid transmission path, material exchange efficiency, and other indicators between these layers can be further analyzed to ensure that the connection between each cementing layer is the most optimized. During the integration process, parameter optimization may also be performed to ensure that the cementing hierarchical connection data can reflect the best connection relationship between layers. Ultimately, through these steps, the cementing hierarchical structure connection data obtained can include information such as the connection relationship of each cementing layer, fluid exchange path, and interaction between layers, providing important data support for subsequent geological engineering decisions and oil and gas production.

[0151] Optionally, step S3 specifically includes:

[0152] Step S31: integrating the wellbore geometric features based on the initial geological model and the cementing engineering topological structure model, thereby obtaining wellbore geometric feature data;

[0153] In this embodiment, the two models are integrated through multi-dimensional data fusion technology to generate the geometric characteristic data of the wellbore. Specifically, the initial geological model provides the formation characteristics of the wellbore at different depths, while the cementing engineering topological structure model provides the geometric shape of the casing and the wellbore wall. Through numerical calculation and simulation technology, combined with this information, the geometric characteristic data of the wellbore, such as the shape of the wellbore wall, the arrangement position of the casing, etc., can be accurately obtained. These data are crucial for the wellbore visualization and fluid simulation in the subsequent steps.

[0154] Step S32: Performing wellbore geometry visualization modeling according to the wellbore geometry feature data, thereby obtaining a wellbore geometry model;

[0155] In this embodiment, the obtained wellbore geometric feature data is used to perform visual modeling through computer-aided design (CAD) software or three-dimensional modeling software (such as AutoCAD, SolidWorks or OpenFlow, etc.). A three-dimensional wellbore model is generated through the geometric feature data of the wellbore (such as well depth, casing thickness, well diameter, well wall structure, etc.). The model can not only show the spatial layout of the wellbore, but also mark information such as stratum distribution and casing position. The geometric information of the wellbore is input through the software, and the spatial relationship between the well wall and the stratum is reflected in the model using a three-dimensional modeling tool. The obtained wellbore geometric model can provide an accurate geometric basis for subsequent fluid flow analysis.

[0156] Step S33: describing the relative position characteristics of the wellbore and the formation based on the wellbore geometric model, thereby obtaining the relative position data of the wellbore and the formation;

[0157] In this embodiment, the relative position between the wellbore and the formation is accurately described through the wellbore geometry model. In the specific operation, the contact points between the wellbore and the different formations are first determined through the wellbore geometry model obtained in the previous step. The depth and position relationship of these contact points will be accurately described as the wellbore-formation relative position data. Using geological exploration data, the different formations (such as sandstone, shale, etc.) passed by the wellbore can be identified, and the relative position of each part of the wellbore and the corresponding formation can be marked. The key to this data is how to accurately describe the interaction between different positions of the wellbore and the formation to ensure that subsequent fluid activity simulation can be carried out.

[0158] Step S34: extracting regional stratigraphic structure characteristics from the regional geological structure characteristic data, thereby obtaining regional stratigraphic structure data;

[0159] In this embodiment, geological exploration data within the regional scope are collected, including information such as lithology, formation dip, fault location, and fracture distribution. These data are generally obtained through geological exploration wells, seismic reflection data, or ground geological surveys. Then, geological modeling tools (such as Petrel, Geographix, etc.) are used to extract and process data, and the original geological data is converted into regional stratigraphic structural characteristic data. This data contains the stratigraphic distribution, structural characteristics, geological structure, etc. of the entire region, and can provide basic information for subsequent wellbore and regional fluid activity models.

[0160] Step S35: constructing a regional fluid activity model based on the regional stratum structure data and the wellbore geometry model according to the wellbore-formation relative position data, thereby obtaining a regional fluid activity model;

[0161] In this embodiment, a model including regional fluid activity characteristics is constructed based on the wellbore-formation relative position data, regional formation structure data and wellbore geometry model. In this process, firstly, the fluid activity path of the wellbore in the regional formation is established according to the relationship between the relative position of the wellbore and the formation. Then, the regional formation structure data is combined with the wellbore geometry model, and a regional fluid activity model is constructed using numerical simulation methods (such as finite element method or fluid dynamics simulation). This model can describe the fluid flow characteristics, pressure changes and the influence of the wellbore on the fluid in the area around the wellbore. This model not only considers the physical properties of the formation, but also the geometry of the wellbore, thereby providing a more accurate prediction of fluid behavior.

[0162] Step S36: performing fluid dynamics simulation according to the regional fluid activity model, thereby obtaining displacement fluid dynamics simulation data;

[0163] In this embodiment, a fluid dynamics simulation is performed based on the constructed regional fluid activity model. Fluid dynamics software (such as COMSOL Multiphysics, ANSYS Fluent, etc.) is used to simulate the dynamic behavior of the fluid under different working conditions. The dynamic characteristics of the fluid, including flow velocity, flow direction, pressure change, etc., will be simulated and calculated. By simulating the fluid activities inside and outside the wellbore, such as the movement and pressure distribution of the displacement fluid, the dynamic simulation data of the displacement fluid are obtained. These data reflect the interaction and changes of the fluid between the wellbore and the formation under different conditions, and provide a basis for the construction of the subsequent fluid characteristic matrix.

[0164] Step S37: constructing a fluid property matrix based on the displacement fluid dynamics simulation data.

[0165] In this embodiment, a fluid property matrix is ​​constructed based on the displacement fluid dynamics simulation data obtained. The fluid property matrix is ​​a multidimensional data structure that contains multiple fluid physical properties (such as viscosity, density, fluidity, etc.) and the relationship between the fluid behavior inside and outside the wellbore. Through data fitting and numerical interpolation technology, the flow characteristics of different fluids in different wellbore geometries and the changing trends under different formation conditions can be extracted. This matrix can be used to optimize wellbore design, fluid injection and production plans, etc., and provide decision support for subsequent wellbore optimization and fluid control.

[0166] Optionally, step S4 is specifically:

[0167] Step S41: selecting regional grid density according to the fluid characteristic matrix, thereby obtaining regional grid density data;

[0168] In this embodiment, by analyzing the relationship between these fluid properties and regions, the grid density requirements of different regions can be obtained. For example, in areas with higher flow rates or larger fluid gradients, the grid density needs to be higher to more accurately simulate the behavior of the fluid. Specifically, by weighted averaging the fluid property matrix, areas where the fluid properties change dramatically, such as boundary layers and areas where the flow velocity changes dramatically, can be determined, and higher density grid division can be performed. Ultimately, the regional grid density data obtained will be used as input for subsequent grid division.

[0169] Step S42: triangulating and meshing the regional fluid activity model based on the regional mesh density data, thereby obtaining an adaptive fluid mesh;

[0170] In this embodiment, an adaptive grid generation algorithm is used to triangulate the fluid region according to the obtained regional grid density data. Assume that in a certain simulation area, based on the fluid characteristic matrix analysis, it is found that the area where the turbulent characteristics of the fluid are more obvious requires a higher grid density, while the area where the flow velocity is relatively stable can choose a lower grid density. Therefore, an adaptive grid algorithm (such as Delaunay triangulation method) is used to divide the area into multiple triangular units, and the size of each triangle is adjusted according to the grid density data. In high-density areas, the triangles will be more detailed and have a higher resolution to capture more fluid details; in low-density areas, the size of the triangles will increase, thereby optimizing computing resources. This adaptive grid generation method allows fluid simulation to maintain a high degree of accuracy in areas where fine calculations are required, while improving computing efficiency in other areas.

[0171] Step S43: extracting the dynamic change characteristics of the fluid state according to the fluid characteristic matrix, thereby obtaining the fluid state dynamic data, and performing fluid variable grid interpolation on the adaptive fluid grid according to the fluid state dynamic data, thereby obtaining the adaptive fluid grid model;

[0172] In the present embodiment, the dynamic change characteristics of the fluid are extracted from the fluid characteristic matrix. This can be achieved by time-space difference method, frequency domain analysis or other suitable dynamic analysis methods. For example, the time-space change characteristics of variables such as fluid velocity, pressure, temperature, density, etc. can be extracted using finite difference method or finite element method. The extraction of dynamic data usually involves modeling the temporal change of the fluid state, such as the time-dependent change of the fluid, or the adjustment of the fluid state based on the external influence of the boundary conditions. Then, according to these dynamic characteristics, the generated adaptive fluid grid is interpolated with fluid variables by interpolation method (such as Lagrange interpolation, spline interpolation or polynomial interpolation). This means that at each node in the grid, the corresponding fluid variables (such as pressure, temperature, etc.) can be obtained, thereby forming an adaptive fluid grid model that can change dynamically. Assume that in a fluid flow simulation, the velocity field and temperature field of the fluid have changed significantly at different time steps. By calculating the change of the fluid state at each time step, and using the interpolation method to transfer the fluid state dynamic data to each node of the adaptive grid, the state of the entire fluid region can be accurately simulated. The interpolation algorithm can effectively combine the high-density grid data and the low-density grid data in the local area to ensure the accuracy and computational efficiency of the fluid simulation.

[0173] Step S44: Perform fluid displacement process simulation according to the adaptive fluid grid model and cementing engineering structure data, so as to obtain fluid displacement process data.

[0174] In this embodiment, the obtained adaptive fluid grid model and cementing engineering structure data are used to simulate the fluid displacement process. Fluid displacement refers to the process in which a new fluid (such as water, mud, oil or other filling liquid) replaces the original fluid in oil and gas production or cementing engineering. In the simulation process, it is first necessary to input the geometric structure data of the cementing engineering (such as well wall, drilling mud, injection fluid and other information). Then, based on the obtained fluid state dynamic data, the flow process of the fluid in the structure is simulated by a numerical simulation model (such as Navier-Stokes equation, mass conservation equation, etc.). By using an adaptive grid, it can be ensured that during the flow process, especially in areas close to the well wall or where the fluid flow changes more drastically, the grid density is higher and the accuracy is higher, so as to obtain more accurate fluid displacement process simulation data. For example, in cementing engineering, cement slurry is injected into the wellbore as a displacement liquid to replace the original fluid. Through adaptive grid simulation, the injection process of cement slurry can be accurately simulated, and the propagation, distribution and interaction of the fluid with the well wall can be observed. By calculating the effects of different injection rates, temperatures and fluid properties on the displacement process, the design of cementing projects can be optimized to ensure the safety and effectiveness of the project.

[0175] Optionally, step S41 is specifically:

[0176] According to the fluid property matrix, the fluid property change amount in the simulation area is counted, so as to obtain the data of the area with high fluid property change amount and the data of the area with low fluid property change amount;

[0177] In this embodiment, the fluid properties (such as velocity, pressure, temperature, etc.) in the simulation area are quantified based on the fluid property matrix. Assuming that the fluid velocity field in the area is to be statistically analyzed, a suitable time step and spatial grid size can be selected to discretize the velocity field data. Then, the flow velocity change in each small grid unit is calculated, and the fluid property change can be: \Delta v = \frac{\left|v_{\text{current}}-v_{\text{previous}}\right|}{\Delta t}, where v_{\text{current}} is the fluid velocity of the current time step, v_{\text{previous}} is the fluid velocity of the previous time step, and \Delta t is the time step. The size of the change is determined based on the change in fluid properties (such as velocity gradient, pressure change, temperature difference, etc.). A larger change indicates that the flow in the area is more complex and requires a higher grid division accuracy.

[0178] The dense grid division density is selected for the data of the area with high fluid characteristic variation, so as to obtain the dense grid area division density data;

[0179] In this embodiment, for areas with large changes (i.e., areas with high fluid property changes), a higher density grid division can be selected. Taking a two-dimensional plane as an example, assuming that in a certain area (such as a section where the flow rate changes dramatically), the grid unit size can be selected to be 0.1mm instead of other larger values. According to the distribution of fluid property changes, these areas can be dynamically selected for refined division. For example, in places where the flow rate changes greatly, the grid size can be set to \Delta x=\Delta y=0.1mm. Conversely, for areas with smaller changes, the grid size can be increased to \Delta x=\Delta y=1mm.

[0180] The sparse grid division density is selected for the data of the area with low fluid characteristic variation, so as to obtain the sparse grid area division density data;

[0181] In this embodiment, for areas with small changes in fluid properties (areas with low changes in fluid properties), a sparser grid division can be selected. For example, in areas with small changes in flow velocity, a grid size of 2 mm or larger can be selected, which can reduce the amount of calculation while maintaining sufficient simulation accuracy. For example, assuming that the temperature gradient in a static area changes very little, the grid size can be selected as \Delta x = \Delta y = 2 mm.

[0182] The dense grid area division density data and the sparse grid area division density data are merged to obtain the regional grid density data.

[0183] In this embodiment, after completing the meshing of the area with high fluid property change and the area with low fluid property change, the next step is to merge the data of the two to form the final mesh density distribution. During the merging process, it is necessary to ensure the smooth transition of the meshing. For the transition from dense grid to sparse grid, an interpolation algorithm can be used to smooth the transition. For example, a smooth transition area is established between the two densities using linear interpolation or quadratic interpolation. In this way, both the accuracy of the calculation is guaranteed and the calculation efficiency is optimized. The merged mesh density data can be expressed as follows: High-density area: the mesh size is \Delta x = \Delta y = 0.1 mm. Transition area: the density range obtained by interpolation. Low-density area: the mesh size is \Delta x = \Delta y = 2 mm. Assume that in a fluid simulation, the velocity changes greatly in a certain section of the fluid area (such as the eddy area or the jet area). The fluid characteristic matrix statistics show that the velocity change is large. This part of the area is divided into a high-density grid with a grid size of 0.1mm; while other areas with smaller velocity changes (such as the boundary layer) are divided into low-density grids with a grid size of 1mm. After merging, the final grid data is as follows: High-density grid area: The grid size is 0.1mm, which is suitable for eddy or complex flow areas. Low-density grid area: The grid size is 1mm, which is suitable for areas with relatively smooth flow. Transition area: The interpolation algorithm is used to smoothly transition the grid density from the high-density area to the low-density area.

[0184] Optionally, step S44 is specifically:

[0185] Extract displacement fluid characteristics from cementing engineering design data to obtain designed displacement fluid data;

[0186] In this embodiment, during the cementing engineering design process, the characteristic data of the displacement fluid generally include important parameters such as the viscosity, density, temperature, and pressure of the fluid. First, collect the basic data for designing the displacement fluid, such as the chemical composition of the fluid, the temperature range, the pressure range, and the expected flow rate, etc. The viscosity and density changes of the fluid at different pressures and temperatures can be measured by fluid testing equipment (such as a rheometer), and the fluid state equation can be generated using the experimental data to extract the dynamic characteristic data of the designed fluid. For example, assuming that the designed displacement fluid is a water-based mud, its viscosity is 5cP and its density is 1.2g / cm2 as measured by a rheometer at 25°C and 1000psi. 3At this point, a state model of the fluid can be constructed, including the viscosity-density relationship under different environmental conditions. This data is then converted into an input format suitable for the simulation model, such as defining the viscosity-temperature-pressure function used in the simulation software.

[0187] The fluid flow boundary conditions are set based on the cementing engineering structure data and the designed displacement fluid data, thereby obtaining the fluid flow boundary conditions;

[0188] In this embodiment, the setting of fluid flow boundary conditions is completed based on the structural data of the cementing project (such as wellbore design, well depth, well section distribution, etc.) and the designed displacement fluid characteristics. First, the flow areas of different well sections are determined by analyzing the geometric structure data of the wellbore. When setting the boundary conditions, factors such as the flow velocity, pressure distribution, and temperature change of the displacement fluid in the well should be taken into account. For example, assuming that the well depth is 4,000 meters, it is designed to use water-based mud fluid for displacement. In this process, boundary conditions can be set according to the depth of the well section and the expected flow velocity. The inlet pressure and temperature of the fluid when entering the well can be calculated by logging data or the formation temperature and pressure model, while the outlet conditions of the fluid (such as flow rate and pressure) can be set according to the limitations of the ground equipment and the wellhead.

[0189] Selecting simulation time parameters according to the fluid state dynamic data, thereby obtaining a simulation time parameter group;

[0190] In this embodiment, the selection of simulation time parameters is determined based on the dynamic characteristics of fluid flow and the speed of change of downhole conditions (such as temperature, pressure, etc.). Generally, the simulation time needs to be long enough to simulate the changes in the entire fluid displacement process, especially the effects of temperature and pressure on fluid viscosity. When setting the simulation time, make sure to capture the stable state of fluid flow. For example, assuming that in a cementing project with a well depth of 4,000 meters, the time it takes for the displacement fluid to reach the bottom of the well from the wellhead is 30 minutes, the simulation may need to be set to 60 minutes so that the fluid can be analyzed as it gradually stabilizes during the entire process. The choice of time step usually affects the simulation accuracy and computational efficiency, so the time step should be optimized based on factors such as the rate of change of the fluid and the change in downhole temperature.

[0191] The fluid displacement process is simulated according to the adaptive fluid grid model, fluid flow boundary conditions and simulation time parameter group, so as to obtain the fluid displacement process data.

[0192] In this embodiment, in the simulation of the fluid displacement process, the use of an adaptive fluid grid model can improve the computational efficiency and simulation accuracy. The adaptive grid model dynamically adjusts the grid density according to the changes in fluid flow, and usually uses finer grids in areas with larger flow gradients (such as areas where the fluid enters deep wells, or areas where the flow rate changes rapidly), and uses coarser grids in areas with smaller changes. For example, when simulating downhole fluid displacement, if the temperature and pressure of the fluid change greatly in certain well sections, these areas can be divided into high-resolution grid areas, while in well sections with smaller temperature and pressure changes, coarser grids can be used. This adaptive grid generation method can effectively improve the accuracy and computational efficiency of simulation calculations. During the simulation process, dynamic simulation of fluid displacement is performed through data such as fluid grid models, flow boundary conditions, and simulation time parameters to simulate fluid behavior under different conditions. For example, the simulation shows the pressure distribution, temperature changes, flow rate changes, etc. of the fluid in different well sections, and how these factors affect the efficiency of the displacement process. Through the above steps, various data in the fluid displacement process can be finally obtained, including the flow state of the fluid in each well section, the pressure and temperature distribution, and the displacement efficiency, etc., providing a scientific basis for the design optimization of cementing operations.

[0193] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0194] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for generating a grid model for a fluid displacement process in a cementing engineering, characterized in that: The following steps are involved: Step S1: Acquire regional geological data of the cementing project, and integrate the regional geological structure characteristics of the regional geological data of the cementing project to obtain regional geological structure characteristic data; Construct an initial geological model based on regional geological structure characteristic data; Step S2: Acquire cementing engineering design data, and extract cementing engineering structural features from the cementing engineering design data, thereby obtaining cementing engineering structural data; perform cementing engineering topological structure modeling based on the cementing engineering structural data, thereby obtaining a cementing engineering topological structure model; Step S3: integrating the wellbore geometric characteristics according to the initial geological model and the cementing engineering topological structure model to obtain wellbore geometric characteristic data, and performing fluid dynamics simulation based on the regional geological structure characteristic data and the wellbore geometric characteristic data to obtain a fluid characteristic matrix; Step S4: performing triangulation dynamic meshing according to the fluid characteristic matrix to obtain an adaptive fluid mesh model, and performing fluid displacement process simulation according to the adaptive fluid mesh model and cementing engineering structure data to obtain fluid displacement process data; Step S5: optimizing the fluid displacement process of the cementing engineering design data based on the fluid displacement process data, thereby obtaining optimized fluid displacement process data, and reallocating the resource configuration of the cementing engineering design data according to the optimized fluid displacement process data, thereby obtaining optimized cementing engineering design data.

2. The grid model generation method for the fluid displacement process of cementing engineering according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: Acquire the regional geological data of the cementing project, and divide the regional geological data of the cementing project into geological characteristics, so as to obtain regional fluid characteristic data and regional soil characteristic data; Step S12: evaluating regional soil permeability according to regional soil characteristic data, thereby obtaining regional soil permeability data; Step S13: performing regional fluid circulation analysis on the regional fluid characteristic data according to the regional soil permeability data, thereby obtaining regional fluid circulation data; Step S14: integrating regional geological structure characteristics based on regional soil permeability data and regional fluid circulation data, thereby obtaining regional geological structure characteristic data; Step S15: construct an initial geological model based on regional geological structure characteristic data.

3. The grid model generation method for the fluid displacement process of cementing engineering according to claim 2 is characterized in that: Step S12 is specifically as follows: Step S121: regional soil characteristic data is divided into regional grids, thereby obtaining grid soil characteristic data; Step S122: extracting soil particle size characteristics and soil porosity characteristics from the grid soil characteristic data, thereby obtaining grid soil particle size data and grid porosity data; Step S123: performing particle size grid distribution statistics according to the grid soil particle size data, thereby obtaining dense grid data of large soil particle sizes and dense grid data of small soil particle sizes; Step S124: performing porosity grid classification based on the grid porosity data, thereby obtaining high soil porosity grid data and low soil porosity grid data; Step S125: performing a grid area intersection operation on the dense grid data of large soil particles and the grid data of high soil porosity, thereby obtaining the regional data of high soil permeability; performing a grid area intersection operation on the dense grid data of small soil particles and the grid data of low soil porosity, thereby obtaining the regional data of low soil permeability; Step S126: calculating soil permeability for the high soil permeability area data and the low soil permeability area data respectively, thereby obtaining permeability values ​​for the high soil permeability area and the low soil permeability area; Step S127: Merging regional spatial data according to the permeability values ​​of the high soil permeability area and the permeability values ​​of the low soil permeability area, thereby obtaining regional soil permeability data.

4. The method for generating a grid model for a cementing engineering fluid displacement process according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: integrating the regional fluid characteristic data with the spatial distribution of fluid soil to obtain regional fluid distribution data, and dividing the regional fluid distribution data into regions to obtain fluid dense distribution region data and fluid sparse distribution region data; Step S132: performing regional soil fluid dynamics simulation according to the regional soil permeability data and the regional fluid characteristic data, thereby obtaining regional soil fluid simulation data; Step S133: Calculating the fluid flow velocity in the permeability region based on the regional soil fluid simulation data, thereby obtaining the fluid flow velocity data in the permeability region, and dividing the fluid flow velocity data in the permeability region into regions, thereby obtaining the fluid high flow velocity region data and the fluid low flow velocity region data; Step S134: performing a regional intersection operation on the fluid dense distribution area data and the fluid high flow rate area data, thereby obtaining high fluid flow area data; Performing a regional intersection operation on the data of the fluid sparsely distributed area and the data of the fluid low flow velocity area, thereby obtaining the data of the low fluid flow area; Step S135: spatially merge the high fluid flowability region data and the low fluid flowability region data to obtain regional fluid flowability data.

5. The grid model generation method for the fluid displacement process of cementing engineering according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: acquiring cementing engineering design data, and extracting cementing engineering structural features from the cementing engineering design data, thereby obtaining cementing engineering structural data; Step S22: classifying the cementing engineering structure data into engineering layer structures, thereby obtaining cementing layer structure data and connection layer structure data; Step S23: integrating the spatial relationship of the cementing layer hierarchy based on the cementing layer structure data, thereby obtaining the cementing layer hierarchy structure data; Step S24: performing cementing hierarchical connection relationship analysis on the cementing layer hierarchical structure data and the connection layer structure data, thereby obtaining cementing hierarchical structure connection data; Step S25: performing cementing engineering topological structure modeling based on the cementing hierarchical structure connection data, thereby obtaining a cementing engineering topological structure model.

6. The method for generating a grid model for a fluid displacement process in a cementing engineering according to claim 5, characterized in that: Step S24 is specifically as follows: Step S241: extracting structural features of the cementing layer hierarchical structure data and the connection layer structure data respectively, thereby obtaining hierarchical structure feature data and connection structure feature data; Step S242: Calculating the structural similarity of the hierarchical structure feature data and the connection structure feature data, thereby obtaining structural similarity data; Step S243: clustering the cementing layer hierarchical structure data and the connection layer structure data according to the structure similarity data, thereby obtaining connection layer-cementing layer clustering data; Step S244: Integrate the cementing hierarchical connection relationship based on the connection layer-cementing layer clustering data, so as to obtain cementing hierarchical structure connection data.

7. The grid model generation method for the fluid displacement process of cementing engineering according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: integrating the wellbore geometric features based on the initial geological model and the cementing engineering topological structure model, thereby obtaining wellbore geometric feature data; Step S32: Performing wellbore geometry visualization modeling according to the wellbore geometry feature data, thereby obtaining a wellbore geometry model; Step S33: describing the relative position characteristics of the wellbore and the formation based on the wellbore geometric model, thereby obtaining the relative position data of the wellbore and the formation; Step S34: extracting regional stratigraphic structure characteristics from the regional geological structure characteristic data, thereby obtaining regional stratigraphic structure data; Step S35: constructing a regional fluid activity model based on the regional stratum structure data and the wellbore geometry model according to the wellbore-formation relative position data, thereby obtaining a regional fluid activity model; Step S36: performing fluid dynamics simulation according to the regional fluid activity model, thereby obtaining displacement fluid dynamics simulation data; Step S37: constructing a fluid property matrix based on the displacement fluid dynamics simulation data.

8. The grid model generation method for the fluid displacement process of cementing engineering according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: selecting regional grid density according to the fluid characteristic matrix, thereby obtaining regional grid density data; Step S42: triangulating and meshing the regional fluid activity model based on the regional mesh density data, thereby obtaining an adaptive fluid mesh; Step S43: extracting the dynamic change characteristics of the fluid state according to the fluid characteristic matrix, thereby obtaining the fluid state dynamic data, and performing fluid variable grid interpolation on the adaptive fluid grid according to the fluid state dynamic data, thereby obtaining the adaptive fluid grid model; Step S44: Perform fluid displacement process simulation according to the adaptive fluid grid model and cementing engineering structure data, so as to obtain fluid displacement process data.

9. The method for generating a grid model for a cementing engineering fluid displacement process according to claim 8, characterized in that: Step S41 is specifically as follows: According to the fluid property matrix, the fluid property change amount in the simulation area is counted, so as to obtain the data of the area with high fluid property change amount and the data of the area with low fluid property change amount; The dense grid division density is selected for the data of the area with high fluid characteristic variation, so as to obtain the dense grid area division density data; The sparse grid division density is selected for the data of the area with low fluid characteristic variation, so as to obtain the sparse grid area division density data; The dense grid area division density data and the sparse grid area division density data are merged to obtain the regional grid density data.

10. The method for generating a grid model for a fluid displacement process in a cementing engineering according to claim 8, characterized in that: Step S44 is specifically as follows: Extract displacement fluid characteristics from cementing engineering design data to obtain designed displacement fluid data; The fluid flow boundary conditions are set based on the cementing engineering structure data and the designed displacement fluid data, thereby obtaining the fluid flow boundary conditions; Selecting simulation time parameters according to the fluid state dynamic data, thereby obtaining a simulation time parameter group; The fluid displacement process is simulated according to the adaptive fluid grid model, fluid flow boundary conditions and simulation time parameter group, so as to obtain the fluid displacement process data.

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