A method for generating a grid model for the displacement process of cementing engineering fluids

Abstract

The present invention relates to the field of numerical simulation technology, and particularly to a method for generating a grid model for the fluid displacement process in the cementing engineering. The method includes the following steps: obtaining geological data of the cementing engineering area and constructing an initial geological model; obtaining the design data of the cementing engineering and performing topological structure modeling of the cementing engineering to obtain a topological structure model of the cementing engineering; performing hydrodynamic simulation based on the initial geological model and the topological structure model of the cementing engineering to obtain a fluid property matrix; performing simulation of the fluid displacement process based on the fluid property matrix to obtain fluid displacement process data; optimizing the fluid displacement process of the cementing engineering design data based on the fluid displacement process data to obtain optimized fluid displacement process data, and reallocating the resource configuration of the cementing engineering design data based on the optimized fluid displacement process data to obtain optimized cementing engineering design data. The present invention can improve the accuracy of fluid displacement simulation.

Description

Technical Field

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

[0002] In the oil and gas industry, cementing engineering is an important step to ensure the structural integrity of oil wells 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, traditional fluid displacement methods have many defects in practical applications, resulting in unsatisfactory cementing effects and even affecting subsequent oil and gas production.

[0003] The 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 the 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, traditional grid generation methods often face problems such as uneven grid division and waste of computing resources when dealing with complex geological conditions and wellbore structures, resulting in insufficient simulation accuracy and efficiency. Traditional simulations of fluid displacement processes 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 complex. For example, the wellbore may have an irregular shape due to geological factors, and the fluid properties may vary due to factors such as temperature and pressure. This simplified assumption severely limits 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 method for generating a grid model for the fluid displacement process in cementing engineering to solve at least one of the above technical problems.

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

[0006] Step S1: Obtain the geological data of the cementing engineering area, integrate the regional geological structure characteristics of the geological data of the cementing engineering area to obtain regional geological structure characteristic data; construct an initial geological model according to the regional geological structure characteristic data;

[0007] Step S2: Obtain the design data of the cementing engineering, extract the cementing engineering structure characteristics of the design data of the cementing engineering to obtain cementing engineering structure data; perform cementing engineering topological structure modeling based on the cementing engineering structure data to obtain a cementing engineering topological structure model;

[0008] Step S3: Integrate the wellbore geometric features based on the initial geological model and the topological structure model of the cementing engineering, so as to obtain the wellbore geometric feature data, and perform hydrodynamic simulation based on the regional geological structure feature data and the wellbore geometric feature data, so as to obtain the fluid property matrix;

[0009] Step S4: Perform triangular meshing dynamic mesh division according to the fluid property matrix, so as to obtain an adaptive fluid mesh model, and perform fluid displacement process simulation according to the adaptive fluid mesh model and the cementing engineering structure data, so as to obtain the fluid displacement process data;

[0010] Step S5: Optimize the fluid displacement process of the cementing engineering design data based on the fluid displacement process data, so as to obtain the optimized fluid displacement process data, and reallocate the resource configuration of the cementing engineering design data according to the optimized fluid displacement process data, so as to obtain the optimized cementing engineering design data.

[0011] By integrating the regional geological structure feature data, the present invention can obtain a more accurate initial geological model. This model provides a solid foundation for subsequent simulations. An accurate geological model can better reflect the actual geological conditions and reduce the risk of cementing failure caused by geological factors. The detailed geological feature analysis provides a basis for the cementing engineering design and helps engineers formulate cementing plans adapted to specific geological conditions. By extracting the structural features of the cementing engineering, the complex engineering design information can be structured, making the subsequent model establishment and analysis more efficient. The generated topological structure model of the cementing engineering can effectively reflect the geometric features of the wellbore and its relationship with the surrounding geology, promoting the understanding of fluid flow behavior. By integrating the wellbore geometric feature data, the fluid flow in the wellbore can be simulated more accurately, improving the reliability of hydrodynamic simulation. Combining the regional geological features and the wellbore geometric data helps to create a fluid property matrix more adaptable to the actual situation and improve the accuracy of the model. Through triangular meshing dynamic mesh division, the complex fluid flow situation can be effectively dealt with, significantly improving the calculation efficiency and simulation accuracy. Using the adaptive fluid mesh model to perform fluid displacement process simulation can accurately predict the fluid flow and mixing conditions, thus optimizing the cementing effect. Based on the analysis of the fluid displacement process data, the fluid displacement process can be identified and optimized, reducing resource waste and improving the overall efficiency. Through the analysis of the optimized process, a reasonable reallocation of resource configuration is carried out, which helps to reduce costs and improve the economy and feasibility of the project. In summary, through the implementation of the above steps, the cementing engineering can not only fully consider the complex geological and fluid properties in the design stage, but also significantly improve the fluid displacement effect through refined dynamic mesh division and simulation analysis. These improvements will help to improve the overall quality of cementing and the efficiency of subsequent oil and gas production, ultimately achieving higher economic benefits and safety.

[0012] Optionally, step S1 is specifically as follows:

[0013] Step S11: Obtain the geological data of the cementing engineering area, and conduct geological feature division on the geological data of the cementing engineering area, so as to obtain regional fluid characteristic data and regional soil characteristic data;

[0014] Step S12: Evaluate the regional soil permeability according to the regional soil characteristic data, so as to obtain regional soil permeability data;

[0015] Step S13: Analyze the regional fluid flowability of the regional fluid characteristic data according to the regional soil permeability data, so as to obtain regional fluid flowability data;

[0016] Step S14: Integrate the regional geological structure characteristics based on the regional soil permeability data and the regional fluid flowability data, so as to obtain regional geological structure characteristic data;

[0017] Step S15: Construct an initial geological model according to the regional geological structure characteristic data.

[0018] The present invention systematically collects the geological data in the region, including rock and soil properties, tectonic characteristics, etc., providing basic information for subsequent analysis. Through geological feature division, the characteristics of different strata and 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 the anti-seepage and water retention capabilities. Through the permeability data, designers can optimize the selection of cementing materials and construction methods to ensure the cementing effect. By analyzing the fluid flowability, the flow characteristics of fluids in the soil can be predicted, and the potential fluid migration paths and speeds can be understood. Identifying areas with changes in fluid flowability helps to evaluate possible environmental risks and formulate corresponding countermeasures. Integrating soil permeability and flowability data helps to comprehensively understand the regional geological structure and identify complex underground environments. By integrating multiple characteristic data, the accuracy and reliability of the subsequent geological model 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 visual geological information for engineers. This model can help the engineering team to 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 as follows:

[0020] Step S121: Conduct regional grid division on the regional soil characteristic data, so as to obtain grid soil characteristic data;

[0021] Step S122: Extract the soil particle size characteristics and soil porosity characteristics from the grid soil characteristic data, so as to obtain the grid soil particle size data and the grid porosity data;

[0022] Step S123: Conduct particle size grid distribution statistics based on the grid soil particle size data, so as to obtain the soil large particle size dense grid data and the soil small particle size dense grid data;

[0023] Step S124: Conduct porosity grid classification based on the grid porosity data, so as to obtain the high soil porosity grid data and the low soil porosity grid data;

[0024] Step S125: Conduct grid area intersection operations on the soil large particle size dense grid data and the high soil porosity grid data, so as to obtain the high soil permeability area data; conduct grid area intersection operations on the soil small particle size dense grid data and the low soil porosity grid data, so as to obtain the low soil permeability area data;

[0025] Step S126: Calculate the soil permeability for the high soil permeability area data and the low soil permeability area data respectively, so as to obtain the high soil permeability area permeability value and the low soil permeability area permeability value;

[0026] Step S127: Conduct regional spatial data merging based on the high soil permeability area permeability value and the low soil permeability area permeability value, so as to obtain the regional soil permeability data.

[0027] Through gridification of the regional soil characteristic data, the present invention can convert continuous soil data into discrete grid data, which is convenient for subsequent analysis and processing. This division makes the spatial distribution of soil characteristics clearer, providing a basis for subsequent feature extraction and statistical analysis. Extracting the particle size characteristics and porosity characteristics of the soil can deeply understand the physical properties of the soil. By statistically analyzing the distribution of grid soil particle sizes, the 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 further provides important information for land use planning, plant growth, and soil and water conservation. Classifying the 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 the intersection operation, the soil areas with high and low permeability can be identified, which helps to determine which areas are suitable for fluid flow. Calculating for different permeability areas can quantify the water passing ability of each area. Merging the data of high and low soil permeability areas forms the overall regional soil permeability data.

[0028] Optionally, step S13 is specifically as follows:

[0029] Step S131: Integrate the regional fluid characteristic data in the fluid-soil spatial distribution to obtain the regional fluid distribution data, and divide the regional fluid distribution data to obtain the fluid dense distribution area data and the fluid sparse distribution area data;

[0030] Step S132: Conduct regional soil fluid dynamics simulation based on the regional soil permeability data and the regional fluid characteristic data to obtain the regional soil fluid simulation data;

[0031] Step S133: Calculate the fluid velocity in the permeability region based on the regional soil fluid simulation data to obtain the fluid velocity data in the permeability region, and divide the fluid velocity data in the permeability region to obtain the fluid high velocity region data and the fluid low velocity region data;

[0032] Step S134: Perform regional intersection operation on the fluid dense distribution area data and the fluid high velocity region data to obtain the high fluid permeability region data; perform regional intersection operation on the fluid sparse distribution area data and the fluid low velocity region data to obtain the low fluid permeability region data;

[0033] Step S135: Perform spatial merging on the high fluid permeability region data and the low fluid permeability region data to obtain the regional fluid permeability data.

[0034] Through the integration of the regional fluid characteristic data, the present invention can obtain detailed fluid distribution conditions, which helps to identify the dense and sparse distributions of fluids in different regions and supports subsequent soil and fluid dynamics research. Such data integration provides a scientific basis for regional management and resource allocation. Combining with the soil permeability data for dynamic simulation can predict the movement behavior of fluids in the soil, providing key data for understanding the interaction between soil and fluids, which is of great significance for water resource management and pollution control. Through the flow velocity calculation, not only can the moving 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. Through the spatial merging of the high permeability and low permeability region data, comprehensive regional fluid permeability data can be formed. This lays a foundation for comprehensively evaluating the regional fluid behavior and its environmental impact, enabling the managers of the cementing project to formulate more reasonable measures.

[0035] Optionally, Step S2 is specifically as follows:

[0036] Step S21: Obtain the cementing project design data, and extract the structural characteristics of the cementing project from the cementing project design data to obtain the cementing project structure data;

[0037] Step S22: Classify the cementing engineering structure data by engineering layer structure to obtain the cementing layer structure data and the connection layer structure data;

[0038] Step S23: Integrate the hierarchical space relationship of the cementing layer based on the cementing layer structure data to obtain the hierarchical structure data of the cementing layer;

[0039] Step S24: Analyze the connection relationship of the cementing layer hierarchy structure data and the connection layer structure data to obtain the connection data of the cementing layer hierarchy structure;

[0040] Step S25: Based on the connection data of the cementing layer hierarchy structure, perform topology structure modeling of the cementing engineering to obtain the topology structure model of the cementing engineering.

[0041] By collecting detailed cementing engineering design data, the present invention can provide accurate basic information for subsequent analysis and processing. This process ensures the comprehensiveness and accuracy of the data, avoiding subsequent errors caused by data missing. Feature extraction and classification of the cementing engineering structure data can clearly distinguish the characteristics of the cementing layer and the connection layer. This classification helps subsequent analysis work, enabling engineers to formulate corresponding design and construction plans for different types of layer structures, thereby improving the pertinence and effectiveness of construction. Integrating the hierarchical space relationship of the cementing layer helps to construct a model of the connection and interaction between layers. This process can reveal the spatial layout and mutual influence between different cementing layers, providing an important basis for subsequent structure analysis and optimization. By analyzing the connection relationship between cementing layer hierarchies, 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 the design and improving the overall performance of the cementing engineering. Based on the obtained data, establishing a topology structure model of the cementing engineering can better visualize and understand the overall structure of the engineering. This model provides a powerful tool for subsequent simulation, analysis and decision-making, enabling engineers to more efficiently perform design optimization and risk assessment.

[0042] Optionally, step S24 is specifically:

[0043] Step S241: Extract the structure features of the cementing layer hierarchy structure data and the connection layer structure data respectively to obtain the hierarchical structure feature data and the connection structure feature data;

[0044] Step S242: Calculate the structure similarity of the hierarchical structure feature data and the connection structure feature data to obtain the structure similarity data;

[0045] Step S243: Cluster the structure features of the cementing layer hierarchy structure data and the connection layer structure data according to the structure similarity data to obtain the connection layer-cementing layer clustering data;

[0046] Step S244: Integrate the connection relationships at the cementing levels based on the connection layer-cementing layer clustering data, so as to obtain the connection data of the cementing level structure.

[0047] By extracting the hierarchical structure of the cementing layer and the structural features of the connection layer, the present invention can filter out key features, reduce data redundancy, and make subsequent analysis more efficient. Obtaining the hierarchical structure features and connection structure features helps to comprehensively understand the geological conditions and technical requirements of cementing, providing a basis for subsequent similarity calculation. Calculating the similarity between different hierarchical structures and connection layers enables the identification of cementing cases with similar geological conditions and engineering requirements. By quantifying the similarity, it provides data support for engineers when selecting appropriate cementing technologies and methods, improving the scientific nature of decision-making. Cluster analysis can discover potential structural patterns, thus effectively classifying different types of cementing layers and connection layers, which is helpful for project 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 relationships of the cementing levels based on the clustering results can reduce structural redundancy, improve the coherence and stability of the overall structure. By optimizing the connection relationships, the compressive capacity and leak prevention performance of cementing can be improved, enhancing the overall safety and reliability of the project.

[0048] Optionally, step S3 is specifically as follows:

[0049] Step S31: Integrate the wellbore geometric features based on the initial geological model and the cementing engineering topological structure model, so as to obtain the wellbore geometric feature data;

[0050] Step S32: Perform wellbore geometric visualization modeling according to the wellbore geometric feature data, so as to obtain the wellbore geometric model;

[0051] Step S33: Describe the relative position features between the wellbore and the formation based on the wellbore geometric model, so as to obtain the relative position data between the wellbore and the formation;

[0052] Step S34: Extract the regional formation structure features from the regional geological structure feature data, so as to obtain the regional formation structure data;

[0053] Step S35: Construct a regional fluid activity model for the regional formation structure data and the wellbore geometric model according to the relative position data between the wellbore and the formation, so as to obtain the regional fluid activity model;

[0054] Step S36: Perform hydrodynamic simulation according to the regional fluid activity model, so as to obtain the displacement fluid hydrodynamic simulation data;

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

[0056] By integrating the initial geological model and the cementing engineering topological structure model, the present invention can accurately extract the geometric feature data of the wellbore. This provides a basis for subsequent modeling, ensures the authenticity and reliability of the model, and helps to avoid engineering mistakes caused by inaccurate geometric features. Visualization modeling can intuitively present the complex geometric features of the wellbore, facilitating engineers and decision-makers to understand the wellbore structure. This intuitive representation helps to conduct more effective design and optimization, improving the operability of the project. Describing the relative position characteristics of the wellbore and the surrounding formation can provide important spatial relationship data for the subsequent analysis of fluid behavior, which helps to identify potential fluid flow channels and optimize the production strategy. Extracting the regional formation structure feature data provides a deeper perspective for understanding the geological environment, which helps to identify geological heterogeneity within the region, improve the evaluation accuracy of oil and gas reservoirs, and support scientific decision-making. Combining the wellbore-formation relative position data and the regional formation structure data to construct a regional fluid activity model enables a more comprehensive understanding of the movement and distribution of fluids in the formation, which helps to evaluate the exploitability of fluid resources and optimize the well location layout. Based on the regional fluid activity model, fluid dynamics simulation can predict the behavior of fluids in the wellbore and the surrounding formation. Through the dynamic data obtained from the simulation, potential problems such as uneven fluid flow can be identified in advance, and preventive measures can be taken. Establishing a fluid property matrix provides a rich data basis for subsequent fluid analysis and optimization. Such a matrix can help engineers evaluate the production efficiency under different fluid conditions and support more scientific decision-making.

[0057] Optionally, step S4 is specifically as follows:

[0058] Step S41: Select the regional grid density according to the fluid property matrix to obtain the regional grid density data;

[0059] Step S42: Perform triangular mesh division on the regional fluid activity model based on the regional grid density data to obtain an adaptive fluid grid;

[0060] Step S43: Extract the dynamic change characteristics of the fluid state according to the fluid property matrix to obtain the dynamic fluid state data, and perform fluid variable grid interpolation on the adaptive fluid grid according to the dynamic fluid state data to obtain an adaptive fluid grid model;

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

[0062] Through the fluid property matrix, the present invention can select appropriate grid densities in different regions according to the properties of the fluid (such as viscosity, density, etc.). This selection avoids using overly fine grids in unnecessary regions, thereby saving computing time and resources. Using a higher grid density in regions with large changes in flow characteristics makes the simulation results more accurate and can better capture the complex behavior of fluid flow. The triangulation method allows the grids to freely adjust their shapes and sizes in different regions, better adapting to complex geometries and flow characteristics. For different characteristics of fluid flow, the grid structure is dynamically adjusted to balance the computing 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. The grid interpolation technology enables the accurate calculation of fluid variables on adaptive grids, thereby enhancing 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 the cementing engineering can be predicted and evaluated, providing a scientific basis for design and implementation.

[0063] Optionally, step S41 is specifically as follows:

[0064] Statistically analyze the change amount of the fluid properties in the simulation region according to the fluid property matrix, so as to obtain the data of the high-fluid-property-change-amount region and the data of the low-fluid-property-change-amount region;

[0065] Select the density of the dense grid division for the data of the high-fluid-property-change-amount region, so as to obtain the density data of the dense grid region division;

[0066] Select the density of the sparse grid division for the data of the low-fluid-property-change-amount region, so as to obtain the density data of the sparse grid region division;

[0067] Merge the density data of the dense grid region division and the density data of the sparse grid region division, so as to obtain the regional grid density data.

[0068] By statistically simulating the changes in fluid characteristics in a region, the present invention can accurately identify regions with high and low change amounts, which is of great significance for the research and application of fluid mechanics. This can help allocate computing resources, concentrate efforts on in-depth analysis in high-change regions, and thus improve the simulation efficiency. In regions where the fluid characteristics change significantly, dense grid division can be used to obtain more accurate flow field distribution information, improve the accuracy of numerical solutions, and capture minute features and complex flow patterns in the flow, such as vortices, boundary layers, etc., providing a more comprehensive flow analysis. In regions with small change amounts, sparse grid division can significantly reduce the computational amount and lower the computational cost. By simplifying the calculation process, the overall simulation speed can be accelerated, making the solution of large-scale fluid dynamics problems more efficient. Merging the data of dense and sparse grids can 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 characteristic changes can be more clearly shown, facilitating the interpretation and application of the results.

[0069] Optionally, step S44 is specifically as follows:

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

[0071] Set the fluid flow boundary conditions based on the cementing engineering structure data and the designed displacement fluid data to obtain the fluid flow boundary conditions;

[0072] Select the simulation time parameters according to the dynamic data of the fluid state to obtain a set of simulation time parameters;

[0073] Perform a simulation of the fluid displacement process according to the adaptive fluid grid model, the fluid flow boundary conditions, and the set of simulation time parameters to obtain the fluid displacement process data.

[0074] By accurately extracting the fluid characteristics, the present invention can select a displacement fluid more suitable for specific cementing conditions, improving the cementing effect. Reasonably selecting the displacement fluid can reduce material costs and improve construction efficiency at the same time. Understanding the fluid characteristics helps to avoid wellbore 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 precisely setting the flow conditions, the fluid behavior can be better predicted and the engineering risks can be reduced. Reasonably selecting the 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 velocities during the fluid displacement process, which helps to deeply analyze 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, helping engineers to 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 following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

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

[0077] Figure 2 It is a detailed schematic flow chart of step S1 in the present invention;

[0078] Figure 3 It is a detailed schematic flow chart of step S2 in the present invention;

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

[0080] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall 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 drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0082] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. 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 the above object, please refer to Figures 1 to 3 , the present invention provides a method for generating a grid model for the displacement process of cementing engineering fluids, and the method includes the following steps:

[0084] Step S1: Obtain the geological data of the cementing engineering area, and integrate the geological structure characteristics of the cementing engineering area to obtain regional geological structure characteristic data; construct an initial geological model according to the regional geological structure characteristic data;

[0085] In this embodiment, the geological data of the cementing engineering area is collected through on-site exploration, drilling, and existing geological investigation reports. These data include formation distribution, lithology change, porosity, permeability, etc. The formation data can be obtained through well logging data, seismic reflection data, and core analysis. Integrate the geological data, analyze the distribution law of different formations, and identify the main geological structures such as faults and folds in the area. Integrate various geological features (such as tectonic units, formation thickness changes, stress fields, etc.) 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, construct an initial three-dimensional geological model. The input data of the model includes formation thickness, fault information, formation hardness, etc., and is simulated through a numerical modeling tool to ensure that the model can reflect the actual geological situation. The details considered in the model include geological horizon distribution, pore structure, and permeability differences, etc.

[0086] Step S2: Obtain the cementing engineering design data, extract the structural characteristics of the cementing engineering from the cementing engineering design data to obtain the cementing engineering structure data; based on the cementing engineering structure data, conduct topological structure modeling of the cementing engineering to obtain the topological structure model of the cementing engineering;

[0087] In this embodiment, the 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.). Extract the wellbore structural characteristics (such as well depth, well diameter change, cementing link design, etc.) from the design data, and obtain other important parameters (such as packers, contact surface between casing and rock formation, etc.) according to the cementing process requirements. Based on the cementing design data, establish a topological structure model of the wellbore. The model should reflect the geometric shape of the wellbore, the connection relationships of 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 topological structure including elements such as casing and annulus.

[0088] Step S3: Integrate the geometric characteristics of the wellbore according to the initial geological model and the topological structure model of the cementing engineering to obtain the wellbore geometric characteristic data, and conduct hydrodynamic simulation based on the regional geological structure characteristic data and the wellbore geometric characteristic data to obtain the fluid property matrix;

[0089] In this embodiment, combine the regional geological model and the topological structure model of the cementing engineering to extract the geometric characteristics of the wellbore (such as the curvature of the wellbore, diameter change, wellbore design, etc.). Use computer-aided design (CAD) tools and 3D modeling software 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. According to the integrated wellbore geometric data and the regional geological structure data, conduct fluid flow simulation. Use CFD (Computational Fluid Dynamics) software (such as ANSYS Fluent, COMSOL, etc.) 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 geometric and geological conditions of the wellbore. According to the hydrodynamic simulation results, calculate the movement trajectory, pressure change, etc. of the fluid in the wellbore. The fluid property matrix will include important properties such as fluid flow rate, pressure distribution, temperature change, etc. in different regions to support the subsequent simulation and optimization processes.

[0090] Step S4: Conduct triangular meshing dynamic grid division according to the fluid property matrix to obtain an adaptive fluid grid model, and conduct simulation of the fluid displacement process according to the adaptive fluid grid model and the cementing engineering structure data to obtain the fluid displacement process data;

[0091] In this embodiment, based on the fluid property matrix, fluid mesh generation is carried out. The adaptive mesh generation algorithm (such as Delaunay triangulation) is used for dynamic mesh generation, and the mesh density is automatically adjusted according to the fluid flow characteristics. In different regions such as the wellbore, annulus, and formation, local mesh refinement is performed according to the velocity change and pressure gradient to improve the calculation accuracy. An adaptive fluid mesh model is generated through dynamic mesh generation to ensure appropriate resolution in different flow regions. The mesh generation should adapt to the velocity and pressure changes of fluid flow, and refined meshes will provide higher calculation accuracy. Based on the adaptive fluid mesh model, the fluid displacement process simulation is carried out in combination with the cementing structure data. The CFD software is used for multiphase fluid simulation to simulate the exchange process of the cementing fluid and the original underground fluid, analyze the efficiency and effect of fluid displacement, and obtain the fluid displacement process data. The changes in conditions such as wellhead flow rate and pressure are considered during the simulation process.

[0092] Step S5: Optimize the fluid displacement process of the cementing engineering design data based on the fluid displacement process data, so as to obtain the optimized fluid displacement process data, and reallocate the resource configuration of the cementing engineering design data according to the optimized fluid displacement process data, so as to obtain the optimized cementing engineering design data.

[0093] In this embodiment, according to the fluid displacement process simulation results, the bottlenecks in fluid flow are identified, and the fluid displacement process is optimized. For example, by adjusting means such as pumping rate, fluid ratio, and additive use, the fluid displacement efficiency is improved. The multi-objective optimization of the displacement process is carried out using optimization algorithms (such as genetic algorithms, 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, and the ratio, pumping parameters, and pressure control of the cementing fluid are optimized to ensure smoother fluid flow during the cementing process 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 the efficient use of resources. A resource optimization model can be used to optimize aspects such as equipment scheduling, material procurement, and personnel allocation, so as to improve the execution efficiency and cost-effectiveness of the entire cementing project.

[0094] Optionally, step S1 is specifically:

[0095] Step S11: Obtain the geological data of the cementing engineering area, and perform geological feature division on the geological data of the cementing engineering area, so as to obtain the regional fluid feature data and the regional soil feature data;

[0096] In this embodiment, geological data of the cementing engineering area is obtained through drilling, geological exploration, and remote sensing technology. The data includes formation thickness, rock type, soil properties, groundwater level, seismic wave velocity, etc. For example, geological reflection wave data of underground layers is obtained using seismic exploration methods (such as reflection seismic exploration), and then geological horizons are determined. During the drilling process, core analysis is used to extract parameters such as rock type, porosity, and permeability of different formations. The obtained data is analyzed and classified, and the area is divided into different geological units according to geological characteristics such as lithology, structure, formation distribution, stress field, etc. For example, unsupervised learning of geological data is performed using machine learning algorithms (such as K-means clustering), and the area is divided into different soil types (such as clay, sand, rock, etc.) according to the physical and chemical properties of the formations. Based on geological characteristics, fluid characteristics in different geological units are deduced, such as hydrogeological conditions, groundwater flow characteristics, oil and gas reservoir distribution, etc. For example, groundwater layers have different fluid properties, such as the viscosity and salinity of water. By analyzing the permeability and porosity of geological layers, the mobility and type of underground fluids are predicted. Based on information such as lithology and soil composition, parameters such as soil permeability, porosity, and water retention capacity are further extracted. For example, the permeability of sand is usually high, while that of clay is low, and these can be obtained through laboratory analysis or geological surveys.

[0097] Step S12: Evaluate the regional soil permeability based on the regional soil characteristic data to obtain the regional soil permeability data;

[0098] In this embodiment, laboratory permeability tests can be conducted on different soil samples in the area to measure the permeability coefficient (K) of the soil. Common methods include conventional hydraulic tests, gas permeability tests, etc. For example, through the use of conventional constant head permeability tests, the permeability of different soil layers under different pressures is measured. Based on the obtained soil sample permeability coefficient data and combined with the regional geological characteristics, the overall soil permeability is evaluated. This can use statistical methods such as multiple regression analysis to build models according to factors such as soil particle composition, water content, and degree of compaction. For example, existing soil permeability data (such as the permeability coefficients of sand, loam, clay, etc.) is used in combination with the geological structure for prediction, so as to evaluate the soil permeability of different regions. According to the evaluation results, a regional soil permeability distribution map is generated, indicating the permeability range of each geological unit, and it is used as the basis for subsequent fluid flow analysis.

[0099] Step S13: Conduct a regional fluid flow analysis on the regional fluid characteristic data based on the regional soil permeability data to obtain the regional fluid flow data;

[0100] In this embodiment, a hydrodynamic model (such as Darcy's law) is used in combination with soil permeability data to analyze the flow characteristics of fluids in the area. The fluidity of the fluid is evaluated based on factors such as the flow velocity, direction, and pressure change of the fluid in different soil layers. For example, the Darcy equation: Q = -K·A·(ΔP / L) is used, 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 area and the physical properties of the fluid (such as water, oil, natural gas, etc.), the flow behavior of the fluid is analyzed to predict the flow path of underground fluids. For example, in high-permeability areas, the fluid will spread rapidly, while in low-permeability areas, the fluid will accumulate or stagnate. Thus, a distribution map of the fluidity in the area is obtained to identify which areas are fluid-rich areas or fluid-stagnant areas, providing decision-making support for the cementing project.

[0101] Step S14: Integrate the regional geological structure characteristics based on the regional soil permeability data and the regional fluidity data to obtain regional geological structure characteristic data.

[0102] In this embodiment, combining the soil permeability and fluidity data, geological modeling software (such as Petrel, GOCAD, etc.) is used to integrate the regional geological characteristics and construct a three-dimensional geological model. For example, based on the known soil layer permeability and fluidity data, combined with the different lithologies, stratigraphic distributions, and tectonic characteristics in the area, the finite element method or simulation technology is used to generate a three-dimensional digital model that reflects the real geological structure. The integrated geological structure characteristic data is analyzed to identify geological anomalies in different areas, such as tectonic characteristics like faults and folds that may affect the cementing quality. For example, by comparing the stress fields, formation thicknesses, and lithology distributions in different areas, potential fractures or other underground structures are predicted, which will affect the distribution and flow of fluids. According to the analysis results, regional geological structure characteristic data, such as underground fault and fracture distribution maps, and the possibility of interlayer sliding, etc., are generated to help avoid potential geological risks during the design of the cementing project.

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

[0104] In this embodiment, based on the integrated regional geological structure characteristic data, an initial geological model is constructed using 3D modeling software. The model should include the spatial distribution of underground rock formations, fluid properties, and geological structures such as potential faults and fractures. 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 preliminarily constructed geological model is verified by comparing it with on-site test data (such as drilling data and geological exploration data) to check the accuracy and reliability of the model. For example, verify whether the predicted permeability and fluid flow characteristics of the model conform to on-site observations, and adjust the model parameters if they do not match. Finally, the construction of the initial geological model is completed, which serves as the basic data for subsequent cementing engineering design, risk assessment, and fluid monitoring.

[0105] Optionally, step S12 is specifically as follows:

[0106] Step S121: Perform regional grid division on the regional soil characteristic data to obtain grid soil characteristic data;

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

[0108] Step S122: Extract the soil particle size characteristics and soil porosity characteristics from the grid soil characteristic data to obtain grid soil particle size data and grid porosity data;

[0109] In this embodiment, based on the obtained grid soil characteristic data, statistical methods are used to extract the soil particle size characteristics (such as the proportions of sand, silt, and clay) and porosity characteristics. The extraction of soil particle size can be carried out by analyzing the mass percentages of different particle sizes in soil samples, and the particle size data is represented by the soil texture (such as sandy soil and clay soil) 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 by remote sensing technology based on the reflection spectral characteristics of the soil. The processed data will be stored as particle size distribution data and porosity data corresponding to the grids for convenient subsequent analysis.

[0110] Step S123: Conduct particle size grid distribution statistics based on the grid soil particle size data, so as to obtain soil large particle size dense grid data and soil small particle size dense grid data;

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

[0112] Step S124: Conduct porosity grid classification based on the grid porosity data, so as to obtain high soil porosity grid data and low soil porosity grid data;

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

[0114] Step S125: Conduct grid area intersection operations on the soil large particle size dense grid data and the high soil porosity grid data, so as to obtain high soil permeability area data; conduct grid area intersection operations on the soil small particle size dense grid data and the low soil porosity grid data, so as to obtain low soil permeability area data;

[0115] In this embodiment, use the GIS platform to perform spatial intersection operations. For the soil large particle size dense grid and the high porosity grid, take the intersection of these two data sets, and the obtained grid represents the high permeability area; similarly, for the small particle size dense grid and the low porosity grid, conduct intersection operations, and the obtained grid represents the low permeability area. The specific steps of the intersection operation are: use the spatial query tool to conduct spatial overlap analysis on the two data sets, and calculate the area where both grid feature conditions are satisfied. For example, the intersection function (Intersection) in QGIS or ArcGIS can be used to intersect the two sets of grid data to obtain the permeability area data.

[0116] Step S126: Calculate the soil permeability for the high soil permeability region data and the low soil permeability region data respectively, so as to obtain the permeability value of the high soil permeability region and the permeability value of the low soil permeability region;

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

[0118] Step S127: Perform regional spatial data merging according to the permeability value of the high soil permeability region and the permeability value of the low soil permeability region, so as to obtain regional soil permeability data.

[0119] In this embodiment, according to the obtained permeability values of each region, the permeability data of the high permeability region and the low permeability region are merged in terms of spatial data. Through the GIS system, the permeability values of all grids are inserted into a unified spatial data structure to achieve the spatial visualization of the permeability data. In this process, interpolation methods (such as Kriging interpolation or inverse distance weighted interpolation) can be used to estimate the permeability values of unmeasured regions, and then a soil permeability distribution map of the entire region is generated.

[0120] Optionally, step S13 is specifically as follows:

[0121] Step S131: Integrate the regional fluid characteristic data for the fluid-soil spatial distribution to obtain regional fluid distribution data, and perform regional division on the regional fluid distribution data to obtain fluid dense distribution region data and fluid sparse distribution region data;

[0122] In this embodiment, the fluid characteristic data of the soil in the area are collected and integrated. These data usually come from geological exploration, remote sensing data, groundwater monitoring stations, soil test 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 capacity, water content, and fluid flow velocity of different soil layers. Subsequently, based on these data, the area can be divided, and clustering analysis methods (such as the K-means algorithm or DBSCAN density clustering) can be used to determine the fluid-dense area and the fluid-sparse area. The dense area is the area with a higher groundwater level or stronger water flow, while the sparse area is the area with less water source or slower fluid flow. Finally, through data visualization means, a diagram of the fluid distribution is obtained to assist in the subsequent steps of area division.

[0123] Step S132: Perform regional soil hydrodynamics simulation based on the regional soil permeability data and the regional fluid characteristic data, so as to obtain regional soil fluid simulation data;

[0124] In this embodiment, based on the regional soil permeability data and the regional fluid characteristic data, soil hydrodynamics simulation is performed. 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, input the soil permeability data in the area (such as the permeability measured based on experiments or the permeability model inferred through remote sensing data). Combining with other physical properties of the soil (such as bulk density, porosity, soil type, etc.), calculations are performed using hydrodynamics equations (such as Darcy's law and the continuity equation). Through this simulation process, the flow situation of the fluid in the soil can be obtained, the transfer, distribution, and flow velocity of the fluid between different soil layers can be evaluated, and the final output simulation data includes information such as the water state, flow path, and potential water flow rate of each soil layer.

[0125] Step S133: Calculate the permeability regional fluid flow velocity based on the regional soil fluid simulation data, so as to obtain the permeability regional fluid flow velocity data, and perform area division on the permeability regional fluid flow velocity data, so as to obtain the fluid high-flow velocity area data and the fluid low-flow velocity area data;

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

[0127] Step S134: Perform a regional intersection operation on the fluid dense distribution region data and the fluid high-velocity region data to obtain the high fluid permeability region data; perform a regional intersection operation on the fluid sparse distribution region data and the fluid low-velocity region data to obtain the low fluid permeability region data.

[0128] In this embodiment, the fluid dense distribution region and the fluid high-velocity region are subjected to an intersection operation to obtain the high fluid permeability region data; at the same time, the fluid sparse distribution region and the fluid low-velocity region are subjected to an intersection operation to obtain the low fluid permeability region data. This process usually relies on GIS software and spatial analysis tools. In GIS, by overlaying layers (such as the fluid distribution map and the velocity map) and using spatial query and overlay analysis functions, the intersection regions of the fluid dense area and the high-velocity area, as well as the intersection regions of the fluid sparse area and the low-velocity area, are found. For the high permeability region, it will appear as a groundwater channel with strong water flow passing ability, while the low permeability region represents the soil area where the water flow is blocked. In this way, more accurate water permeability zoning information is obtained, providing support for subsequent decisions such as land use and resource management.

[0129] Step S135: Perform a spatial merge on the high fluid permeability region data and the low fluid permeability region data to obtain the regional fluid permeability data.

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

[0131] Optionally, step S2 is specifically as follows:

[0132] Step S21: Obtain the cementing engineering design data and extract the cementing engineering structure features from the cementing engineering design data to obtain the cementing engineering structure data;

[0133] In this embodiment, during the cementing engineering design stage, designers will use CAD software (such as AutoCAD) or specialized oil and gas exploration and production design tools (such as Petrel or OpenWells) to draw cementing engineering drawings and engineering design plans. These design data include relevant information such as wellbores, casings, cementing mud, cementing density, wellhead elevation, etc. Through a data interface or API, these design data are exported from the design software and undergo data cleaning and formatting processing to extract the basic structure features of the cementing engineering, such as wellbore depth, casing material and thickness, cementing process parameters (such as cement slurry density, gelling property, pressure requirements), etc. These data are stored as cementing engineering structure data for subsequent analysis and calculation.

[0134] Step S22: Classify the cementing engineering structure data by engineering layer structure to obtain the cementing layer structure data and the connection layer structure data;

[0135] In this embodiment, after obtaining the cementing engineering structure data, when performing layer structure classification, it is first necessary to divide the well sections according to the well depth and design requirements. By analyzing the well section data, different cementing layers (such as surface layer, production layer, and isolation layer) are identified, and the depth range where each cementing layer is located 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, formula of the cementing fluid, etc. At the same time, the connection layers (the connection layers are usually isolation layers or transition layers) between different cementing layers are separately extracted and saved as the connection layer structure data.

[0136] Step S23: Integrate the spatial relationships of the cementing layer hierarchy based on the cementing layer structure data to obtain the cementing layer hierarchy structure data;

[0137] In this embodiment, when integrating the hierarchical spatial relationships based on the cementing layer structure data, it is first necessary to clarify the physical relationships between the cementing layers, such as the upper and lower positions of each layer, the relative distances between layers, and the relative positions of the casings. The integration of these spatial relationships can be achieved through the fusion of geological exploration data and wellbore data. By means of 3D modeling software, the wellbore is virtually reconstructed to display the spatial positions and structural relationships of each cementing layer. For example, the relative positions of the cementing layers and casings are marked using CAD or 3D modeling tools and compared with the existing formation structure diagrams to ensure the reasonable spatial structure of each cementing layer. After data integration, the generated hierarchical structure data of the cementing layers can be presented in the form of graphs or charts and saved in a standardized data format for subsequent analysis.

[0138] Step S24: Analyze the connection relationships of the cementing layer hierarchical structures and the connection layer structures to obtain the connection data of the cementing layer hierarchical structures;

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

[0140] Step S25: Based on the connection data of the cementing layer hierarchical structures, perform topological structure modeling of the cementing project to obtain the topological structure model of the cementing project.

[0141] In this embodiment, when modeling the topological structure of the cementing project based on the cementing hierarchical structure connection data, the topological relationships of each cementing layer and connection layer need to be considered first. This can be achieved through the graph structure modeling method in graph theory. Each cementing layer is regarded as a node in the graph, and the connection relationship between the cementing layers is regarded as the edge between the nodes. By constructing a graph composed of nodes and edges, the topological structure of the cementing project is represented. Further, the topological structure model needs to be verified through numerical calculations 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 each layer, 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 like STL and STEP, for simulation, testing, and further optimization in actual engineering.

[0142] Optionally, step S24 is specifically as follows:

[0143] Step S241: Extract the structural features of the cementing layer hierarchical structure data and the connection layer structure data respectively, so as to obtain the hierarchical structure feature data and the connection structure feature data;

[0144] In this embodiment, it is necessary to analyze the data of the cementing layer and the connection layer in detail and extract their structural features. The hierarchical structure data of the cementing layer usually includes the depth, thickness, geological properties, porosity, permeability, etc. of the layer, while the connection layer structure data includes the connection method between different cementing layers, the fluid transmission channel, the substance exchange situation, 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 properties 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 extraction of the connection layer structure data can be based on the fracture and crack conditions between different layers and the continuity of the fluid path. By using means such as CT scanning and geological survey, the density change and crack distribution between each connection layer can be obtained. Through these methods, the obtained hierarchical structure feature data of the cementing layer can include the properties of each cementing layer and its relative position in the geological column; the connection layer structure feature data includes the substance exchange characteristics between different layers and the continuity of the connection channel, etc.

[0145] Step S242: Calculate the structural similarity of the hierarchical structure feature data and the connection structure feature data, so as to obtain the structural similarity data;

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

[0147] Step S243: Perform structure feature clustering on the cementing layer hierarchy data and the connection layer structure data according to the structure similarity data, so as to obtain connection layer - cementing layer clustering data;

[0148] In this embodiment, according to the calculated structure similarity data, a clustering algorithm is used to perform clustering analysis on the cementing layer hierarchy and the connection layer structure data. Common clustering methods include K - means clustering, hierarchical clustering (such as agglomerative clustering), DBSCAN, etc. Suppose the similarity matrix between each cementing layer and the connection layer has been calculated, then the K - means clustering algorithm can be used to cluster these hierarchical structure data next. The K - means algorithm will allocate the hierarchical data to different clusters according to the similarity values between layers. For example, suppose the cementing layer data is divided into 3 clusters by K - means clustering, and each cluster represents a type of cementing layer with a similar structure; similarly, the structure data of the connection layer is also clustered in a similar way. The clustering result assigns a class identifier to each cementing layer and its corresponding connection layer, forming the clustering data between the connection layer and the cementing layer. In this way, it can be clearly understood which connection layers are strongly related to which cementing layers.

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

[0150] In this embodiment, based on the clustering data of the connection layer and the cementing layer obtained in the previous step, the integration of the cementing layer-level structure connection is carried out. The core objective 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 categories of each cementing layer are corresponding to the clustering categories 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 association relationships, the connection relationship integration of the cementing layer level is carried out through optimization algorithms (such as graph optimization algorithms, minimum spanning tree algorithms, etc.). For example, assume 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, indicators such as the fluid transmission path and material exchange efficiency between these levels can be further analyzed to ensure the most optimized connection between each cementing layer. During the integration process, parameter optimization may also be carried out to ensure that the cementing layer-level connection data can reflect the best connection relationship between layers. Finally, through these steps, the obtained cementing layer-level structure connection data can include information such as the connection relationship of each cementing layer, the fluid exchange path, and the interaction between layers, providing important data support for subsequent geological engineering decisions and oil and gas exploitation.

[0151] Optionally, step S3 is specifically as follows:

[0152] Step S31: Integrate the wellbore geometric features based on the initial geological model and the cementing engineering topological structure model to obtain wellbore geometric feature data;

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

[0154] Step S32: Carry out wellbore geometric visualization modeling according to the wellbore geometric feature data to obtain a wellbore geometric model;

[0155] In this embodiment, by using the obtained wellbore geometric feature data, visual modeling is carried out 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 based on the wellbore geometric feature data (such as well depth, casing thickness, well diameter, wellbore structure, etc.). This model can not only display the spatial layout of the wellbore but also mark information such as formation distribution and casing position. The geometric information of the wellbore is input through the software, and the spatial relationship between the wellbore wall and the formation is reflected in the model by using three-dimensional modeling tools. The obtained wellbore geometric model can provide an accurate geometric basis for subsequent fluid flow analysis.

[0156] Step S33: Describe the relative position characteristics of the wellbore - formation based on the wellbore geometric model, so as to obtain the wellbore - formation relative position data;

[0157] In this embodiment, the relative position between the wellbore and the formation is accurately described through the wellbore geometric model. In specific operations, first, the contact points between the wellbore and different formations are determined through the wellbore geometric model obtained in the previous step. The depths and positional relationships of these contact points will be accurately described as wellbore - formation relative position data. By using geological exploration data, different formations (such as sandstone, shale, etc.) penetrated by the wellbore can be identified, and the relative position of each part of the wellbore with the corresponding formation can be marked. The key to this data lies in how to accurately describe the interaction between different positions of the wellbore and the formation to ensure that subsequent fluid activity simulations can be carried out.

[0158] Step S34: Extract the regional formation structure characteristics from the regional geological structure feature data, so as to obtain the regional formation structure data;

[0159] In this embodiment, geological exploration data within the regional scope are collected, including information such as lithology, formation dip angle, fault position, and fracture distribution. These data are generally obtained through geological exploration wells, seismic reflection data, or surface geological surveys. Then, geological modeling tools (such as Petrel, Geographix, etc.) are used for data extraction and processing to convert the original geological data into regional formation structure feature data. This data includes the formation distribution, structural characteristics, and geological structures of the entire region, and can provide basic information for subsequent wellbore and regional fluid activity models.

[0160] Step S35: Construct a regional fluid activity model based on the wellbore - formation relative position data, the regional formation structure data, and the wellbore geometric model, so as to obtain the regional fluid activity model;

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

[0162] Step S36: Conduct fluid dynamics simulation according to the regional fluid activity model to obtain displacement fluid dynamics simulation data;

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

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

[0165] In this embodiment, according to the obtained displacement fluid dynamics simulation data, a fluid property matrix is constructed. The fluid property matrix is a multi - dimensional data structure that includes various 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 techniques, the flow characteristics of different fluids under different wellbore geometric forms and the change 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 - making support for subsequent wellbore optimization and fluid control.

[0166] Optionally, step S4 is specifically as follows:

[0167] Step S41: Select the regional grid density according to the fluid property matrix to obtain regional grid density data;

[0168] In this embodiment, by analyzing the relationship between these fluid characteristics and regions, the grid density requirements for different regions can be obtained. For example, in regions with a larger flow velocity or a larger fluid gradient, a higher grid density is required to more accurately simulate the behavior of the fluid. Specifically, by performing a weighted average on the fluid characteristic matrix, regions where the fluid properties change drastically, such as the boundary layer and regions with a sharp change in flow velocity, can be determined for grid division with a higher density. Finally, the obtained regional grid density data will be used as input for subsequent grid division.

[0169] Step S42: Based on the regional grid density data, perform triangular meshing on the regional fluid activity model to obtain an adaptive fluid grid;

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

[0171] Step S43: Extract the dynamic change characteristics of the fluid state according to the fluid characteristic matrix to obtain the dynamic data of the fluid state, and perform fluid variable grid interpolation on the adaptive fluid grid according to the dynamic data of the fluid state to obtain an adaptive fluid grid model;

[0172] In this embodiment, the dynamic change characteristics of the fluid are extracted from the fluid property matrix. This can be achieved through spatio-temporal difference methods, frequency domain analysis, or other suitable dynamic analysis methods. For example, the finite difference method or the finite element method can be used to extract the spatio-temporal change characteristics of variables such as fluid velocity, pressure, temperature, and density. The extraction of dynamic data usually involves modeling the temporal variation of the fluid state, such as the time-dependent change of the fluid, or the adjustment of the fluid state based on external influences of boundary conditions. Then, based on these dynamic characteristics, through interpolation methods (such as Lagrange interpolation, spline interpolation, or polynomial interpolation), the fluid variables are interpolated for the generated adaptive fluid grid. This means that at each node in the grid, the corresponding fluid variables (such as pressure, temperature, etc.) can be obtained, thus forming an adaptive fluid grid model that can dynamically change. Suppose in a fluid flow simulation, the velocity field and temperature field of the fluid change 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 dynamic data of the fluid state 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 low-density grid data in the local area, ensuring the accuracy and computational efficiency of the fluid simulation.

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

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

[0175] Optionally, step S41 is specifically:

[0176] Statistically analyze the change in the fluid properties in the simulation region according to the fluid property matrix, so as to obtain the data of the region with high fluid property change and the data of the region with low fluid property change;

[0177] In this embodiment, according to the fluid property matrix, the fluid properties (such as velocity, pressure, temperature, etc.) in the simulation region are quantified. Suppose we want to statistically analyze the fluid velocity field in the region. We can select a suitable time step and spatial grid size to discretize the velocity field data. Then, calculate the change in the flow velocity within each small grid cell. The change in fluid properties can be: \(\Delta v=\frac{\left|v_{\text{current}} - v_{\text{previous}}\right|}{\Delta t}\), where \(v_{\text{current}}\) is the fluid velocity at the current time step, \(v_{\text{previous}}\) is the fluid velocity at the previous time step, and \(\Delta t\) is the time step. Determine the magnitude of the change according to the change in fluid properties (such as velocity gradient, pressure change, temperature difference, etc.). A larger change indicates that the flow in this region is more complex and requires a higher grid division accuracy.

[0178] Select the density of the dense grid division for the data of the region with high fluid property change, so as to obtain the data of the density of the dense grid region division;

[0179] In this embodiment, for the region with a large change (i.e., the region with high fluid property change), a higher density grid division can be selected. Taking a two-dimensional plane as an example, suppose in a certain region (such as a section with a large change in flow velocity), the grid cell size can be selected as 0.1 mm instead of other larger values. According to the distribution of the change in fluid properties, these regions can be dynamically selected for refined division. For example, where the flow velocity changes greatly, the grid size can be set as \(\Delta x=\Delta y = 0.1\) mm. On the contrary, for the region with a small change, the grid size can be increased to \(\Delta x=\Delta y = 1\) mm.

[0180] Select the density of the sparse grid division for the data of the region with low fluid property change, so as to obtain the data of the density of the sparse grid region division;

[0181] In this embodiment, for the region with a small change in fluid properties (the region with low fluid property change), a sparser grid division can be selected. For example, in the region with a small change in flow velocity, a grid size of 2 mm or larger can be selected, which can reduce the calculation amount while maintaining sufficient simulation accuracy. For instance, suppose the temperature gradient change in a certain static region is extremely small, the grid size can be selected as \(\Delta x=\Delta y = 2\) mm.

[0182] Merge the density data of the dense grid area division and the density data of the sparse grid area division to obtain the regional grid density data.

[0183] In this embodiment, after the grid division of the high fluid property change amount area and the low fluid property change amount area is completed, the next step is to merge the data of the two to form the final grid density distribution. During the merging process, it is necessary to ensure a smooth transition of the grid division. For the transition from a dense grid to a sparse grid, an interpolation algorithm can be used to achieve a smooth transition. For example, linear interpolation or quadratic interpolation methods can be used to establish a smooth transition region between the two densities. In this way, both the calculation accuracy and the calculation efficiency are optimized. The merged grid density data can be represented by the following method: High-density area: The grid size is \(\Delta x=\Delta y = 0.1\mathrm{mm}\). Transition area: The density range obtained by interpolation. Low-density area: The grid size is \(\Delta x=\Delta y = 2\mathrm{mm}\). Assume that in a certain fluid simulation, the velocity changes greatly in a certain section of the fluid area (such as the eddy current area or the injection area). Through the statistical analysis of the fluid property matrix, it is found that the velocity change amount is large. This part of the area is divided into high-density grids with a grid size of \(0.1\mathrm{mm}\); while other areas with relatively small velocity changes (such as the boundary layer) are divided into low-density grids with a grid size of \(1\mathrm{mm}\). After merging, the final grid data is as follows: High-density grid area: The grid size is \(0.1\mathrm{mm}\), suitable for eddy current or complex flow areas. Low-density grid area: The grid size is \(1\mathrm{mm}\), suitable for areas with relatively stable flow. Transition area: Smooth transition through the interpolation algorithm, so that the grid density gradually transitions from the high-density area to the low-density area.

[0184] Optionally, step S44 is specifically:

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

[0186] In this embodiment, during the cementing engineering design process, the characteristic data of the displacement fluid usually includes important parameters such as the viscosity, density, temperature, and pressure of the fluid. First, collect the basic data of the designed displacement fluid, such as the chemical composition, temperature range, pressure range, and expected flow rate of the fluid. The viscosity and density changes of the fluid at different pressures and temperatures can be measured by fluid test equipment (such as a rheometer), and the fluid state equation can be generated using the experimental data to further extract the dynamic characteristic data of the designed fluid. For example, assume that the designed displacement fluid is a certain water-based mud. The viscosity is measured as \(5\mathrm{cP}\) and the density is \(1.2\mathrm{g / cm}\) at \(25^{\circ}C\) and \(1000\mathrm{psi}\) by a rheometer. 3At this time, a state model of the fluid can be constructed, including the viscosity-density relationship under different environmental conditions. Then, this data is converted into an input format suitable for the simulation model, such as defining a function of viscosity-temperature-pressure used in the simulation software.

[0187] Set the fluid flow boundary conditions based on the cementing engineering structure data and the designed displacement fluid data, so as to obtain the fluid flow boundary conditions;

[0188] In this embodiment, the setting of the fluid flow boundary conditions is completed according to the structure data of the cementing engineering (such as wellbore design, well depth, well section distribution, etc.) and the characteristics of the designed displacement fluid. First, by analyzing the geometric structure data of the wellbore, the flow regions of different well sections are determined. When setting the boundary conditions, factors such as the flow velocity, pressure distribution, and temperature change of the displacement fluid in the well need to be considered. For example, assume that the well depth is 4000 meters and water-based mud fluid is designed for displacement. During this process, the boundary conditions can be set according to the depth of the well section and the expected flow velocity. The inlet pressure and temperature when the fluid enters the well can be deduced through well logging data or formation temperature and pressure models, while the outlet conditions of the fluid (such as flow velocity, pressure) can be set according to the limitations of surface equipment and the wellhead.

[0189] Select the simulation time parameters according to the dynamic data of the fluid state, so as to obtain a set of simulation time parameters;

[0190] In this embodiment, the selection of the simulation time parameters is determined based on the dynamic characteristics of the fluid flow and the change rate of downhole conditions (such as temperature, pressure, etc.). Usually, the simulation time needs to be long enough to simulate the changes in the entire fluid displacement process, especially the influence of temperature and pressure on the fluid viscosity. When setting the simulation time, it is necessary to ensure that the stable state of the fluid flow can be captured. For example, assume that in a cementing project with a well depth of 4000 meters, the time for the displacement fluid to reach the bottom of the well from the wellhead is 30 minutes, and the simulation may need to be set to 60 minutes to analyze the gradual stabilization of the fluid during the entire process. The selection of the time step usually affects the simulation accuracy and calculation efficiency, so the time step should be optimized according to factors such as the change rate of the fluid and the downhole temperature change.

[0191] Carry out the simulation of the fluid displacement process according to the adaptive fluid grid model, the fluid flow boundary conditions, and the set of simulation time parameters, 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 mesh model can improve the calculation efficiency and simulation accuracy. The adaptive mesh model dynamically adjusts the mesh density according to the changes in fluid flow. Usually, finer meshes are used in regions with large flow gradients (such as the region where the fluid enters the deep well or the region where the flow velocity changes rapidly), while coarser meshes are used in regions with smaller changes. For example, during the downhole fluid displacement simulation, if in certain well sections, the temperature and pressure of the fluid change significantly, these regions can be divided into high-resolution mesh regions, while in well sections with smaller temperature and pressure changes, coarser meshes can be used. This way of generating adaptive meshes can effectively improve the accuracy and calculation efficiency of the simulation. During the simulation process, dynamic simulation of fluid displacement is carried out through data such as the fluid mesh model, flow boundary conditions, and simulation time parameters to simulate the fluid behavior under different conditions. For example, the simulation shows the pressure distribution, temperature change, flow velocity change, 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 during the fluid displacement process can finally be obtained, including the flow state of the fluid in each well section, pressure and temperature distribution, and displacement efficiency, etc., providing a scientific basis for the design optimization of the cementing operation.

[0193] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0194] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can 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 these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating a grid model for the displacement process of cementing engineering fluids, characterized in that, It includes the following steps: Step S1: Obtain the geological data of the cementing engineering area, and integrate the regional geological structure characteristics of the cementing engineering area geological data to obtain regional geological structure characteristic data; Construct an initial geological model according to the regional geological structure characteristic data; Step S2: Obtain the cementing engineering design data, and extract the cementing engineering structure characteristics of the cementing engineering design data to obtain the cementing engineering structure data; Based on the cementing engineering structure data, conduct cementing engineering topological structure modeling to obtain the cementing engineering topological structure model; Step S3: Integrate the wellbore geometric characteristics according to the initial geological model and the cementing engineering topological structure model to obtain the wellbore geometric characteristic data, and conduct hydrodynamic simulation based on the regional geological structure characteristic data and the wellbore geometric characteristic data to obtain the fluid property matrix; Step S4: Conduct triangular meshing dynamic grid division according to the fluid property matrix to obtain an adaptive fluid grid model, and conduct fluid displacement process simulation according to the adaptive fluid grid model and the cementing engineering structure data to obtain the fluid displacement process data; Step S5: Optimize the fluid displacement process of the cementing engineering design data based on the fluid displacement process data to obtain the optimized fluid displacement process data, and reallocate the resource configuration of the cementing engineering design data according to the optimized fluid displacement process data to obtain the optimized cementing engineering design data.

2. The method for generating a grid model for the displacement process of cementing engineering fluids according to claim 1, wherein Step S1 is specifically: Step S11: Obtain the geological data of the cementing engineering area, and divide the geological characteristics of the cementing engineering area geological data to obtain regional fluid characteristic data and regional soil characteristic data; Step S12: Evaluate the regional soil permeability according to the regional soil characteristic data to obtain the regional soil permeability data; Step S13: Analyze the regional fluid flowability of the regional fluid characteristic data according to the regional soil permeability data to obtain the regional fluid flowability data; Step S14: Integrate the regional geological structure characteristics based on the regional soil permeability data and the regional fluid flowability data to obtain the regional geological structure characteristic data; Step S15: Construct an initial geological model according to the regional geological structure characteristic data.

3. The method for generating a grid model for the displacement process of cementing engineering fluids according to claim 2, wherein Step S12 is specifically: Step S121: Conduct regional grid division on the regional soil characteristic data to obtain grid soil characteristic data; Step S122: Extract the soil particle size characteristics and soil porosity characteristics of the grid soil characteristic data to obtain the grid soil particle size data and the grid porosity data; Step S123: Conduct particle size grid distribution statistics according to the grid soil particle size data to obtain the soil large particle size dense grid data and the soil small particle size dense grid data; Step S124: Conduct porosity grid classification based on the grid porosity data to obtain the high soil porosity grid data and the low soil porosity grid data; Step S125: Perform grid area intersection operations on the large soil particle dense grid data and the high soil porosity grid data to obtain high soil permeability area data; perform grid area intersection operations on the small soil particle dense grid data and the low soil porosity grid data to obtain low soil permeability area data; Step S126: Calculate the soil permeability for the high soil permeability area data and the low soil permeability area data respectively to obtain the permeability values of the high soil permeability area and the low soil permeability area; Step S127: Perform regional spatial data merging based on the permeability values of the high soil permeability area and the low soil permeability area to obtain regional soil permeability data.

4. The method for generating a grid model for the displacement process of a cementing engineering fluid according to claim 2, wherein Step S13 is specifically as follows: Step S131: Integrate the fluid-soil spatial distribution of the regional fluid characteristic data to obtain regional fluid distribution data, and perform regional division on the regional fluid distribution data to obtain fluid dense distribution area data and fluid sparse distribution area data; Step S132: Conduct regional soil fluid dynamics simulation based on the regional soil permeability data and the regional fluid characteristic data to obtain regional soil fluid simulation data; Step S133: Calculate the fluid velocity in the permeability area based on the regional soil fluid simulation data to obtain fluid velocity data in the permeability area, and perform regional division on the fluid velocity data in the permeability area to obtain fluid high velocity area data and fluid low velocity area data; Step S134: Perform grid area intersection operations on the fluid dense distribution area data and the fluid high velocity area data to obtain high fluid conductivity area data; Perform grid area intersection operations on the fluid sparse distribution area data and the fluid low velocity area data to obtain low fluid conductivity area data; Step S135: Perform spatial merging on the high fluid conductivity area data and the low fluid conductivity area data to obtain regional fluid conductivity data.

5. The method for generating a grid model for the displacement process of a cementing engineering fluid according to claim 1, wherein Step S2 is specifically as follows: Step S21: Obtain the cementing engineering design data, and extract the structural characteristics of the cementing engineering design data to obtain cementing engineering structure data; Step S22: Classify the engineering layer structure of the cementing engineering structure data to obtain cementing layer structure data and connecting layer structure data; Step S23: Integrate the hierarchical spatial relationship of the cementing layers based on the cementing layer structure data to obtain cementing layer hierarchical structure data; Step S24: Analyze the connection relationship of the cementing layer hierarchy for the cementing layer hierarchical structure data and the connecting layer structure data to obtain cementing layer hierarchical structure connection data; Step S25: Build a topological structure model of the cementing engineering based on the cementing layer hierarchical structure connection data to obtain a topological structure model of the cementing engineering.

6. The method for generating a grid model for the displacement process of a cementing engineering fluid according to claim 5, wherein Step S24 is specifically as follows: Step S241: Extract the structural characteristics of the cementing layer hierarchical structure data and the connecting layer structure data respectively to obtain hierarchical structure characteristic data and connecting structure characteristic data; Step S242: Calculate the structural similarity of the hierarchical structure feature data and the connection structure feature data to obtain the structural similarity data; Step S243: Cluster the structural features of the cementing layer hierarchical structure data and the connection layer structure data according to the structural similarity data to obtain the connection layer-cementing layer clustering data; Step S244: Integrate the connection relationship of the cementing layer hierarchy based on the connection layer-cementing layer clustering data to obtain the connection data of the cementing layer hierarchy structure.

7. The method for generating a grid model for the displacement process of a cementing engineering fluid according to claim 1, wherein Step S3 specifically includes: Step S31: Integrate the wellbore geometric features based on the initial geological model and the cementing engineering topological structure model to obtain the wellbore geometric feature data; Step S32: Perform wellbore geometry visualization modeling according to the wellbore geometric feature data to obtain the wellbore geometry model; Step S33: Describe the relative position features of the wellbore and the formation based on the wellbore geometry model to obtain the relative position data of the wellbore and the formation; Step S34: Extract the regional formation structure features from the regional geological structure feature data to obtain the regional formation structure data; Step S35: Construct a regional fluid activity model for the regional formation structure data and the wellbore geometry model according to the relative position data of the wellbore and the formation to obtain the regional fluid activity model; Step S36: Perform hydrodynamic simulation according to the regional fluid activity model to obtain the displacement fluid hydrodynamic simulation data; Step S37: Construct a fluid property matrix based on the displacement fluid hydrodynamic simulation data.

8. The method for generating a grid model for the displacement process of a cementing engineering fluid according to claim 1, wherein Step S4 specifically includes: Step S41: Select the regional grid density according to the fluid property matrix to obtain the regional grid density data; Step S42: Perform triangular meshing on the regional fluid activity model based on the regional grid density data to obtain an adaptive fluid grid; Step S43: Extract the dynamic change characteristics of the fluid state according to the fluid property matrix to obtain the dynamic fluid state data, and perform fluid variable grid interpolation on the adaptive fluid grid according to the dynamic fluid state data to obtain the adaptive fluid grid model; Step S44: Perform a simulation of the fluid displacement process according to the adaptive fluid grid model and the cementing engineering structure data to obtain the fluid displacement process data.

9. The method for generating a grid model for the displacement process of a cementing engineering fluid according to claim 8, characterized in that Step S41 specifically includes: Statistical analysis of the fluid property change amount in the simulation area according to the fluid property matrix to obtain the high fluid property change amount area data and the low fluid property change amount area data; Select the dense grid division density for the high fluid property change amount area data to obtain the dense grid area division density data; Select the sparse grid division density for the low fluid property change amount area data to obtain the sparse grid area division density data; Merge the dense grid area division density data and the sparse grid area division density data to obtain the regional grid density data.

10. The method for generating a grid model for the displacement process of cementing engineering fluids according to claim 8, wherein Step S44 specifically includes: Extract the displacement fluid characteristics from the cementing engineering design data to obtain the designed displacement fluid data; Set the fluid flow boundary conditions based on the cementing engineering structure data and the designed displacement fluid data to obtain the fluid flow boundary conditions; Select simulation time parameters based on dynamic fluid state data to obtain a set of simulation time parameters; Perform a fluid displacement process simulation based on the adaptive fluid grid model, fluid flow boundary conditions, and the set of simulation time parameters to obtain fluid displacement process data.

Images