Residual oil distribution rule analysis method

Abstract

The invention relates to the technical field of data analysis, in particular to a remaining oil distribution rule analysis method. The method comprises the following steps: acquiring oilfield exploitation position information data and oilfield drilling data; seismic wave reflection signals are collected according to the oilfield exploitation position information data, and standard seismic wave reflection signals are obtained; performing four-dimensional geologic structure dynamic monitoring on the standard seismic wave reflection signal by using the oilfield drilling data so as to generate oilfield underground formation structure dynamic monitoring data; performing underground oil-gas distribution analysis according to the dynamic monitoring data of the underground formation structure of the oil field to generate an underground oil-gas distribution diagram; and performing mining remaining oil gas difference calculation on the underground oil gas distribution diagram to generate remaining oil gas mining data. Through dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification and systematized mining strategy construction, the precision and practicability of residual oil distribution rule analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular, to a method for analyzing the distribution law of remaining oil. Background Art

[0002] At first, the research on remaining oil mainly relied on empirical analysis and simple physical models. At this stage, geologists had a preliminary understanding of the formation and evolution of oil reservoirs through the basic knowledge of stratigraphy and sedimentology, but lacked a systematic analysis of the distribution law of remaining oil. With the development of technology, especially after the 1970s, the progress of seismic exploration technology made the identification of formation structures more accurate. During this period, researchers began to use seismic data to invert reservoir characteristics and then reveal the distribution pattern of remaining oil. At the same time, the application of numerical simulation technology made dynamic reservoir simulation possible and was able to better predict the behavior of remaining oil. With the improvement of computing power and the development of data processing technology, machine learning and data mining methods were gradually introduced into the analysis of remaining oil. Using big data technology, researchers were able to process complex multi-dimensional data sets and thus discover the distribution law of remaining oil hidden in the data. However, currently, the existing technologies often lack a systematic method when analyzing the flow path of remaining oil and are unable to effectively identify the high-aggregation areas of oil volume flow, resulting in relatively low accuracy and practicability in the analysis of the distribution law of remaining oil. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for analyzing the distribution law of remaining oil to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for analyzing the distribution law of remaining oil, the method includes the following steps: Step S1: Obtain the oilfield production location information data and the oilfield drilling data; collect seismic wave reflection signals according to the oilfield production location information data to obtain standard seismic wave reflection signals; use the oilfield drilling data to perform four-dimensional geological structure dynamic monitoring on the standard seismic wave reflection signals, thereby generating oilfield underground formation structure dynamic monitoring data; Step S2: Perform underground oil and gas distribution analysis according to the oilfield underground formation structure dynamic monitoring data to generate an underground oil and gas distribution map; calculate the difference in remaining oil and gas production for the underground oil and gas distribution map to generate remaining oil and gas production data; perform regional grid division on the underground oil and gas distribution map based on the remaining oil and gas production data to generate an underground oil and gas residual area grid; perform remaining oil flow path analysis on the underground oil and gas residual area grid to generate remaining oil grid flow path data; Step S3: Adjacent grid splicing is performed on the grids of the underground oil and gas residual area according to the remaining oil grid flow path data to generate a spliced grid for the underground oil and gas residual area flow; oil quantity aggregation calculation is performed on the spliced grid for the underground oil and gas residual area flow to generate an oil quantity flow aggregation; high-aggregation-degree areas are marked on the spliced grid for the underground oil and gas residual area flow through the oil quantity flow aggregation, thereby generating high-aggregation-degree areas of oil quantity flow. Step S4: Effective area screening is performed on the high-aggregation-degree areas of oil quantity flow to obtain data on the effective areas of high-aggregation-degree residual oil; analysis of the remaining oil distribution law is performed on the data of the effective areas of high-aggregation-degree residual oil to generate analysis data on the remaining oil distribution law; based on the analysis data on the remaining oil distribution law, a residual oil exploitation strategy is constructed for the oilfield drilling data, thereby generating a residual oil exploitation strategy.

[0005] The present invention ensures the basic accuracy of the analysis and monitoring data by obtaining the oilfield exploitation location information and oilfield drilling data. This process lays a solid data foundation for subsequent seismic wave reflection signal acquisition and dynamic monitoring. Using standard seismic wave reflection signals for four-dimensional geological structure dynamic monitoring can capture the changes in the underground structure of the oilfield in real time, ensuring the timeliness and accuracy of the monitoring data and providing an important basis for subsequent oil and gas distribution analysis. Based on the dynamic monitoring data of the underground formation structure for oil and gas distribution analysis helps to generate a more accurate underground oil and gas distribution map, thereby providing effective visual support and data basis for subsequent exploitation decisions. By calculating the difference between the exploited oil and gas and the remaining oil in the underground oil and gas distribution map, the exploitation effect and the remaining resource status can be evaluated in a timely manner, providing data support for adjusting the exploitation strategy. Gridifying the oil and gas distribution map into regions to generate grids of the underground oil and gas residual area helps to achieve refined management and regional division, enhancing the spatial management ability of underground resources. Analysis of the remaining oil flow path can reveal the dynamic distribution and flow trend of oil and gas underground, providing important clues for formulating a scientific exploitation strategy and reducing resource waste. Calculating the oil quantity flow aggregation and marking high-aggregation-degree areas can effectively identify priority exploitation areas, improving the exploitation efficiency and economic benefits and ensuring the continuous profitability of the oilfield. Screening the effective areas of high-aggregation-degree areas ensures that exploitation activities are concentrated in economically viable areas, thus avoiding unnecessary resource waste and environmental damage. The exploitation strategy formulated based on the analysis data of the remaining oil distribution law ensures the sustainable development of the oilfield resources, making the oilfield development more scientific and reasonable and improving the overall exploitation efficiency. Therefore, the present invention improves the accuracy and practicality of the remaining oil distribution law analysis through dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification, and systematic exploitation strategy construction.

[0006] Preferably, Step S1 includes the following steps: Step S11: Obtain oilfield exploitation location information data and oilfield drilling data; Step S12: Transmit seismic waves according to the oilfield exploitation location information data and record synchronously to obtain seismic wave reflection signals; perform signal preprocessing on the seismic wave reflection signals to generate standard seismic wave reflection signals, where the signal preprocessing includes signal denoising, signal normalization, and signal detrending processing; Step S13: Use the oilfield drilling data to perform three-dimensional modeling of the underground formations of the oilfield on the standard seismic wave reflection signals to generate three-dimensional modeling data of the underground formations of the oilfield; Step S14: Perform four-dimensional dynamic monitoring on the three-dimensional modeling data of the underground formations of the oilfield to generate dynamic monitoring data of the underground formation structure of the oilfield.

[0007] By obtaining the oilfield exploitation location information and drilling data, the present invention ensures the accuracy of the data, providing reliable basic data for subsequent seismic wave transmission and signal processing. By performing denoising, normalization, and detrending processing on the seismic wave reflection signals to generate standardized signals, the accuracy of the data can be significantly improved, which helps to build a more refined formation structure model. Using the standard seismic wave reflection signals and drilling data to construct a three-dimensional model of the underground formations of the oilfield makes the analysis of the geological structure more intuitive, providing an accurate formation structure map for decision-making. By performing four-dimensional dynamic monitoring in Step S14, the underground structure changes can be updated in real time, providing support for risk prediction and optimization in oilfield exploitation, and improving the safety and effectiveness of exploitation. Through high-precision dynamic monitoring data, engineers can be helped to evaluate the exploitation status in a timely manner and adjust the exploitation strategy, thereby improving the efficiency and resource utilization rate of oilfield exploitation.

[0008] Preferably, Step S13 includes the following steps: Perform logging velocity analysis on the oilfield drilling data to generate oilfield logging velocity data; perform seismic velocity inversion on the standard seismic wave reflection signals according to the oilfield logging velocity data to generate seismic wave velocity data in the oilfield area; perform reflection wave travel time picking on the standard seismic wave reflection signals to generate seismic reflection wave trend point data; perform depth linear conversion on the seismic reflection wave trend point data and the seismic wave velocity data in the oilfield area to generate geological layer depth information data; Perform Fourier transform on the standard seismic wave reflection signals to generate seismic wave reflection spectrum data; perform reflection interface identification on the seismic wave reflection spectrum data according to the geological layer depth information data to obtain underground layer geological interface data; Extract formation attribute information from the underground layer geological interface data to obtain formation attribute information data; perform layer modeling based on the reflection interface on the underground layer geological interface data to generate three-dimensional formation structure framework data; perform initial formation attribute assignment on the three-dimensional formation structure framework data through the formation attribute information data to generate three-dimensional modeling data of the underground formations of the oilfield.

[0009] The present invention generates logging velocity data through logging velocity analysis, ensuring accurate inversion of seismic wave reflection signals, improving the accuracy of seismic wave velocity data, and thus better reflecting the actual situation of underground formations. Perform depth linear conversion on the reflection wave trend point data to obtain geological layer depth information, effectively eliminating the depth error caused by seismic velocity differences and providing accurate formation depth data for subsequent modeling. Use Fourier transform to generate seismic wave reflection spectrum data and combine depth information for reflection interface identification, which can effectively distinguish different formation interfaces, improve the resolution of layer interfaces, and help identify complex geological structures. The extracted formation attribute information data not only covers geological features but also provides an accurate data basis for further attribute assignment, contributing to the refined description of formation attributes and improving the accuracy of modeling. Construct a three-dimensional formation structure framework model based on formation interface data and generate a complete three-dimensional formation model through attribute assignment, making the underground formation structure of the oilfield more intuitive and detailed, providing a clear structure model for geological engineering personnel. The generated three-dimensional underground formation model of the oilfield can dynamically reflect the attributes and structure of the formation, facilitating real-time optimization of decisions in oilfield exploration, development, and management, and improving the development efficiency and safety of oilfield resources. Step S13 not only enhances the recognition accuracy of geological layers but also constructs a high-precision and information-rich three-dimensional formation model through multi-level data fusion and inversion processing, providing important support for the optimized development and effective management of oilfield resources.

[0010] Preferably, step S14 includes the following steps: Step S141: Confirm the time reference point for the three-dimensional modeling data of the underground formation of the oilfield to obtain monitoring time node data; collect repeated seismic exploration data for the three-dimensional modeling data of the underground formation of the oilfield based on the monitoring node time data to obtain a seismic exploration time series dataset; Step S142: Perform seismic data difference analysis on the seismic exploration time series dataset to generate a time-difference formation change profile; perform formation attribute comparison analysis on the time-difference formation change profile to generate a formation attribute change distribution map; Step S143: Dynamically update the geological data of the three-dimensional modeling data of the underground formation of the oilfield through the formation attribute change map to generate dynamic monitoring data of the underground formation structure of the oilfield, where the dynamic geological data update includes dynamic update of the structural form and dynamic update of the attributes.

[0011] Through the confirmation of the time reference point and the setting of the monitoring time node, the present invention can ensure consistent seismic data acquisition for the oilfield formation under the same time sequence, provide a reliable time reference for the monitoring of formation changes, and enhance the comparability of the monitoring data. Through the analysis of the seismic time sequence data difference in step S142, a time-difference formation change profile is generated to help identify the formation changes between different time nodes, and then a formation attribute change distribution map is generated. This difference analysis helps to quickly identify the minor geological changes occurring in the oilfield and effectively warns of potential problems. In step S143, the formation attribute change map is used to dynamically update the geological data, including the real-time update of the structural form and attributes, so that the three-dimensional underground model of the oilfield can reflect the latest state of the formation. This update method ensures the dynamic adaptability of the model and improves the scientificity of decision-making in the oilfield development process. The dynamic monitoring data can help geological engineers timely evaluate the impact of oilfield development on the formation structure, providing data support for optimizing the exploitation plan, reducing resource waste, and reducing environmental impact. The dynamically updated formation data can provide safety guarantees during the exploitation process, help identify potential geological risks, and provide real-time support for the safety of oilfield operations.

[0012] Preferably, step S2 includes the following steps: Step S21: Analyze the underground oil and gas distribution based on the dynamic monitoring data of the underground formation structure of the oilfield to generate an underground oil and gas distribution map; calculate the total oil and gas storage volume of the underground oil and gas distribution map to obtain the total oil and gas storage volume data; calculate the oil and gas production volume of the underground oil and gas distribution map based on the total oil and gas storage volume data to obtain the oil and gas production volume data; Step S22: Calculate the data difference between the total oil and gas storage volume data and the oil and gas production volume data to generate the remaining oil and gas production data; compare the remaining oil and gas production data with the preset residual oil and gas reserve threshold. When the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, the underground oil and gas distribution map is regionally meshed based on the remaining oil and gas production data to generate an underground oil and gas residual area grid; Step S23: Analyze the regional geological hardness of the underground oil and gas residual area grid through the dynamic monitoring data of the underground formation structure of the oilfield to generate grid regional geological hardness data; use the grid regional geological hardness data to analyze the remaining oil flow path of the underground oil and gas residual area grid to generate remaining oil grid flow path data.

[0013] Through the analysis of underground oil and gas distribution and the calculation of the total oil and gas storage, this invention provides an accurate resource distribution map and reserve data for oilfield exploitation, facilitating the precise grasp of the distribution and exploitation volume of oil and gas resources and realizing more scientific resource management. Through the calculation of the remaining oil and gas data in step S22 and the threshold comparison, the remaining reserves of the oilfield are monitored in real time to ensure timely adjustment of the exploitation strategy when the reserves are close to the preset threshold, avoiding resource waste and improving the exploitation efficiency. When the remaining oil and gas reserves are lower than the threshold, the system will conduct regional grid segmentation on the oil and gas distribution, making the management of low-reserve areas more refined, helping to concentrate on the residual resource areas with higher exploitation efficiency, and improving the resource utilization rate. The geological hardness analysis in step S23 provides an assessment of the geological difficulty of the grid area, providing a scientific basis for the drilling and exploitation difficulty during the exploitation process, helping to avoid risks, reduce the exploitation difficulty and cost. Using the geological hardness data of the grid area to analyze the flow path of the remaining oil helps to plan the optimal exploitation path, facilitating the efficient extraction of residual oil resources and improving the ultimate recovery rate of oil and gas resources. Based on the refined management of dynamic monitoring and regional analysis, it ensures that the exploitation process is safer and more controllable, reduces resource waste, optimizes the exploitation strategy, and enhances the efficiency and economy of oilfield operations.

[0014] Preferably, compare the remaining oil and gas exploitation data with the preset residual oil and gas reserve threshold. When the remaining oil and gas exploitation data is less than or equal to the residual oil and gas reserve threshold, the regional grid division of the underground oil and gas distribution map based on the remaining oil and gas exploitation data includes: Compare the remaining oil and gas exploitation data with the preset residual oil and gas reserve threshold. When the remaining oil and gas exploitation data is less than or equal to the residual oil and gas reserve threshold, conduct the initial grid cell division on the underground oil and gas distribution map based on the remaining oil and gas exploitation data to generate the initial grid cell data, where the initial grid cell data includes formation attributes and oil and gas attributes; Calculate the grid occupancy ratio of the formation attributes and oil and gas attributes to obtain the formation-oil and gas grid occupancy ratio data; balance the attribute occupancy ratio of the initial grid cell data according to the formation-oil and gas grid occupancy ratio data to generate the internal adjustment data of the grid cell; conduct the dynamic displacement analysis of the oil and gas on the oil and gas attributes to generate the dynamic displacement data of the oil and gas; Conduct the adaptive adjustment of the grid boundary on the initial grid cell data according to the dynamic displacement data of the oil and gas, thereby generating the external adjustment data of the grid cell; allocate the grid numbers to the initial grid cell data based on the internal adjustment data of the grid cell and the external adjustment data of the grid cell, thereby generating the grid of the underground oil and gas residual area.

[0015] Through the comparison of remaining oil and gas production data with thresholds and the initial grid cell division, the system can automatically identify low-reserve areas and conduct fine-grained grid management, facilitating the precise positioning and exploitation of remaining resources and achieving more refined control of oil and gas distribution. The calculation of the proportion of formation and oil and gas properties and their balance adjustment in the steps ensure the rationalization of the formation and oil and gas distribution ratios within each grid cell, contributing to the accurate assessment of oil and gas reserves in the area, avoiding deviations in resource estimation, and enhancing the scientific nature of the management of the remaining resources in the oilfield. Through the dynamic displacement analysis of oil and gas properties, dynamic displacement data of oil and gas are generated, which helps predict the flow and diffusion paths of oil and gas within the grid cells, enabling more precise exploitation operations and reducing resource waste caused by uncertain flow. The adaptive adjustment of grid boundaries based on the dynamic displacement data of oil and gas ensures that the grid boundaries can be updated in real time with the dynamic changes of oil and gas, making the boundary accuracy of each grid cell higher and further optimizing the resource recovery rate. Numbering and allocation are carried out for the grid cell data after internal and external adjustments to form an underground oil and gas residual area grid with unique identifiers, facilitating subsequent management and data retrieval, and improving the traceability and visualization of oilfield exploitation information. The dynamically adjusted grid management and refined flow path analysis significantly improve the exploitation rate of remaining oil and gas resources, while optimizing the exploitation paths and strategies, reducing the costs and environmental impacts of oilfield exploitation, and enhancing the economic efficiency and sustainability of oilfield operations. The grid management and dynamic optimization of oilfield exploitation are effectively supported, realizing the efficient utilization and scientific management of residual oil and gas resources and contributing to the efficient exploitation and precise management of oilfield resources.

[0016] Preferably, step S23 includes the following steps: Step S231: Calculate the geological hardness of the underground oil and gas residual area grid through the dynamic monitoring data of the underground formation structure of the oilfield to generate grid area geological hardness data; the formula for geological hardness calculation is as follows:

[0017] In the formula, represents the geological hardness, represents the elastic modulus, represents the rock density; Step S232: Screen the low-hardness grids of the underground oil and gas residual area grid according to the grid area geological hardness data to obtain low-hardness screened grids; conduct geological porosity analysis on the low-hardness screened grids to generate low-hardness grid geological porosity data; Step S233: Simulate the remaining oil and gas flow paths of the underground oil and gas residual area grid through the low-hardness grid geological porosity data to generate oil and gas flow path simulation data; Step S234: Visualize the path of the oil and gas flow path simulation data to generate remaining oil grid flow path data.

[0018] The present invention uses the geological hardness calculation formula Quantifying geological hardness and parameterizing rock hardness in grid areas help identify low-hardness areas, provide a basis for dynamic stratification management, and improve adaptability to different geological areas during oil and gas production. By screening low-hardness area grids, the system can focus on low-hardness areas with higher porosity, achieve directional resource exploitation, reduce unnecessary drilling and exploration work, and reduce the difficulty and cost of oil and gas production. After geological porosity analysis of low-hardness grids, the flow characteristics of oil and gas in residual areas are further clarified. Porosity data helps to identify the mobility of resources in underground grids, thereby optimizing the flow path of oil and gas and improving the recovery rate of remaining oil and gas resources. Based on the flow path simulation of low-hardness grid geological porosity data, oil and gas flow path simulation data is generated, enabling production personnel to predict and optimize the flow path of oil and gas, reduce resource waste and maximize production effects. Through the visualization of the flow path, the generated residual oil grid flow path data can intuitively display the flow trend and direction of oil and gas, providing comprehensive visualization data support for oil field production, and facilitating the formulation of accurate production plans in complex geological structures. Through low-hardness screening and path optimization, over-exploitation of high-hardness, low-porosity areas is avoided, which not only saves mining resources but also reduces environmental impact and improves the environmental friendliness and sustainability of the oil field.

[0019] Preferably, step S3 comprises the following steps: Step S31: marking the flow boundary points of the underground oil and gas residual regional grid according to the residual oil grid flow path data to obtain the regional grid oil and gas flow boundary points; using the regional grid oil and gas flow boundary points to splice the adjacent grids of the underground oil and gas residual regional grid to generate the underground oil and gas residual regional flow splicing grid; Step S32: connect the remaining oil grid flow path data through the underground oil and gas residual area flow splicing grid to generate a remaining oil flow path network; perform oil volume aggregation calculation on the remaining oil flow path network according to gravity acceleration to generate oil volume flow aggregation; wherein the formula for oil volume aggregation calculation is as follows:

[0020] In the formula, Expressed as the amount of flowing oil, Expressed as oil density, Expressed as the acceleration due to gravity, Expressed as the flow cross-sectional area, Expressed as flow height, Expressed as the viscosity of the oil; Step S33: Return the oil flow aggregation amount to Step S22 and compare it again with the preset residual oil and gas reserve threshold. When the oil flow aggregation amount is greater than or equal to the preset residual oil and gas reserve threshold, mark the high-aggregation-degree area for the underground oil and gas residual area flow splicing grid based on the oil flow aggregation amount, so as to generate the high-aggregation-degree area of oil flow.

[0021] Through the marking of the flow boundary points, the system of the present invention can accurately identify the boundary area of oil and gas flow, providing a basis for the refined management of the remaining oil and gas flow, avoiding resource leakage, and ensuring the stability of the flow path. The splicing of adjacent grids ensures the continuity of the flow of residual oil and gas between regional grids, thereby generating a flow splicing grid. This splicing method can effectively connect the flow paths of oil and gas in different regions and avoid the problem of flow restriction caused by path interruption. Calculate the flow aggregation amount of the remaining oil through the oil aggregation calculation formula The formula takes into account multiple parameters such as the flow cross-sectional area, flow height, oil density, and viscosity, making the aggregation calculation result more accurate, thereby providing a data basis for efficient exploitation. According to the comparison between the flow aggregation amount and the residual oil and gas reserve threshold, mark the high-aggregation-degree area to ensure that the system gives priority to the grid areas with rich oil volume. This method not only improves the output of oil and gas exploitation but also greatly improves the resource utilization efficiency. The feedback of the aggregation amount and the re-comparison with the threshold ensure that the system can make dynamic adjustments according to the actual data, realizing the continuous monitoring and optimization of the oil and gas exploitation process. At the same time, this loop comparison mechanism provides flexible strategy adjustments for different exploitation stages, ensuring the effectiveness of the exploitation plan. Step S3 performs refined processing in terms of resource aggregation and path planning, making the exploitation activities more targeted and cost-effective. By identifying and aggregating high-concentration oil and gas areas, the waste of resources in inefficient exploitation is greatly reduced, and the exploitation economy is improved.

[0022] Preferably, Step S4 includes the following steps: Step S41: Monitor the residual oil in the high-aggregation-degree area of oil flow to generate high-aggregation-degree area residual oil monitoring data; Step S42: Screen the effective area of the high-aggregation-degree area of oil flow according to the high-aggregation-degree area residual oil monitoring data to obtain the residual oil high-aggregation-degree effective area data; Step S43: Analyze the distribution law of the remaining oil for the residual oil high-aggregation-degree effective area data to generate the residual oil distribution law analysis data; construct a residual oil exploitation strategy for the oilfield drilling data based on the residual oil distribution law analysis data, thereby generating a residual oil exploitation strategy.

[0023] The high-aggregation regional residual oil monitoring data generated by the regional residual oil monitoring of the present invention can identify effective regions with high residual oil aggregation, effectively reducing the exploitation of inefficient regions and providing data support for precise oil and gas exploitation. Further screening of the effective regions for the high-aggregation regions yields data on effective regions with high residual oil aggregation. This screening process ensures that resources are concentrated on regions rich in residual oil, reducing unnecessary resource waste and improving the economic efficiency of exploitation. By analyzing the distribution law of residual oil in the effective regions, the system can predict and master the spatial distribution characteristics of residual oil, and the generated data on the analysis of the distribution law of residual oil provides a scientific basis for subsequent exploitation, ensuring that the exploitation strategy is based on actual data and enhancing the accuracy of the exploitation strategy. Using the data on the analysis of the distribution law of residual oil and combining with the data of oilfield drilling information to construct a residual oil exploitation strategy makes the exploitation strategy more in line with the actual oilfield situation. This strategy fundamentally improves the efficiency and output of residual oil recovery. By constructing a customized residual oil exploitation strategy, the development of non-effective regions is reduced. This development targeting effective regions with high aggregation not only increases the output of residual oil but also reduces the costs of ineffective drilling and exploration. The regional residual oil monitoring and the construction of the residual oil exploitation strategy form a dynamic feedback mechanism, ensuring that the exploitation process can be optimized and adjusted according to real-time monitoring data. This dynamic monitoring and strategy iteration mechanism enables the oilfield development to maintain high efficiency and scientific nature at all stages.

[0024] Preferably, the analysis of the remaining oil distribution law for the data of effective regions with high residual oil aggregation includes: Extracting the regional residual oil characteristics from the data of effective regions with high residual oil aggregation to obtain the data on the residual oil characteristics of effective regions with high aggregation; dividing the data on the residual oil characteristics of effective regions with high aggregation into data sets to generate a model training set and a model test set; training the model training set through a decision tree algorithm to generate a pre-model for predicting the distribution law of residual oil; Optimizing and iterating the pre-model for predicting the distribution law of residual oil according to the model test set to generate a prediction model for the distribution law of residual oil; importing the data of effective regions with high residual oil aggregation into the prediction model for the distribution law of residual oil to analyze the distribution law of residual oil, thereby generating data on the analysis of the distribution law of residual oil.

[0025] The present invention extracts the residual oil characteristics of the highly aggregated effective area, ensuring the accuracy and representativeness of the model input data, thereby providing a high-quality data basis for the analysis of the residual oil distribution law. The residual oil characteristic data is divided into data sets to generate a training set and a test set, ensuring the training and test effects of the model, enabling the model to have higher generalization ability, and thus improving the prediction accuracy. The decision tree algorithm is used to train the training set to generate a prediction pre-model for the residual oil distribution law. The decision tree algorithm has good interpretability and high efficiency, making the model more efficient in predicting the residual oil distribution law. The prediction pre-model is optimized and iterated through the test set, enabling the model to continuously adjust and optimize parameters, and finally generating a high-precision prediction model for the residual oil distribution law, laying a solid foundation for subsequent data analysis. After importing the highly aggregated effective area data into the prediction model, the residual oil distribution law analysis data is generated, revealing the spatial distribution trend and characteristics of the residual oil. This data provides strong support for formulating precise exploitation strategies, ensuring that the exploitation activities are well-founded. By accurately grasping the residual oil distribution law, the exploitation plan can be implemented for the high oil volume aggregation areas, significantly improving the development efficiency of the oilfield, while reducing the development investment in the low-efficiency areas, further improving the resource utilization rate and economic return. The prediction model helps to identify and preferentially exploit the high residual oil aggregation areas, avoiding blind exploitation, and enabling the oilfield resources to be more reasonably managed and utilized.

[0026] The beneficial effects of the present invention are as follows: By obtaining the oilfield exploitation location information data and oilfield drilling data, the basic integrity of the data is ensured, providing an accurate geographical and technical background for subsequent analysis. By collecting standard seismic wave reflection signals, the formation structure of the oilfield can be clearly identified, promoting more accurate underground imaging. Real-time dynamic monitoring of the geological structure changes in the oilfield helps to adjust the exploitation strategy and optimize the resource allocation in a timely manner. The underground oil and gas distribution map generated based on the dynamic monitoring data can provide the spatial distribution information of oil and gas, helping to identify the oil and gas enrichment areas. Calculating the exploitation data of the remaining oil and gas helps to evaluate the development potential and economic benefits of the oilfield. Regional gridification of the underground oil and gas distribution map can refine the analysis, making the subsequent flow path analysis more accurate. Defining the flow path of the remaining oil helps to optimize the exploitation strategy and improve the recovery rate of the remaining oil. By splicing adjacent grids, the data of different regions can be integrated to form a more comprehensive flow splicing grid of the oil and gas residual area. Accurately calculating the oil flow aggregation volume provides a basis for identifying high-aggregation areas in the subsequent process. Marking the high-aggregation areas helps to quickly locate the most valuable areas for development and improve the exploitation efficiency. By screening the high-aggregation areas of oil flow, the efficiency of development resources is ensured, and ineffective exploitation is avoided. Analyzing the effective high-aggregation areas can reveal the distribution characteristics of the remaining oil and optimize the subsequent exploitation strategy. The residual oil exploitation strategy formulated based on the analysis data makes the exploitation process more scientific and systematic. Therefore, through dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification, and systematic exploitation strategy construction, the present invention improves the accuracy and practicability of the analysis of the remaining oil distribution law. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic flow chart of the steps of a method for analyzing the remaining oil distribution law; Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in Figure 4 is Figure 1 a detailed implementation step flow chart of step S4 in The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED IMPLEMENTATION MANNER

[0028] 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 part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0029] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent 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 can 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.

[0030] It should be understood that although terms such as "first" and "second" may be used here 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 can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0031] To achieve the above object, please refer to Figures 1 to 4 , a method for analyzing the distribution law of remaining oil, the method comprising the following steps: Step S1: Obtain oilfield production location information data and oilfield drilling data; collect seismic wave reflection signals according to the oilfield production location information data to obtain standard seismic wave reflection signals; use the oilfield drilling data to perform four-dimensional geological structure dynamic monitoring on the standard seismic wave reflection signals, thereby generating oilfield underground formation structure dynamic monitoring data; Step S2: Analyze the underground oil and gas distribution according to the oilfield underground formation structure dynamic monitoring data to generate an underground oil and gas distribution map; calculate the difference in remaining oil and gas production from the underground oil and gas distribution map to generate remaining oil and gas production data; perform regional grid division on the underground oil and gas distribution map based on the remaining oil and gas production data to generate an underground oil and gas residual area grid; analyze the remaining oil flow path of the underground oil and gas residual area grid to generate remaining oil grid flow path data; Step S3: Adjacent grid splicing is performed on the underground oil and gas residual area grids according to the remaining oil grid flow path data to generate underground oil and gas residual area flow splicing grids; oil quantity aggregation calculation is performed on the underground oil and gas residual area flow splicing grids to generate oil quantity flow aggregation; high-aggregation degree areas are marked on the underground oil and gas residual area flow splicing grids through the oil quantity flow aggregation, thereby generating high-aggregation degree areas of oil quantity flow. Step S4: Effective area screening is performed on the high-aggregation degree areas of oil quantity flow to obtain residual oil high-aggregation degree effective area data; analysis of the remaining oil distribution law is performed on the residual oil high-aggregation degree effective area data to generate residual oil distribution law analysis data; based on the residual oil distribution law analysis data, a residual oil exploitation strategy is constructed for the oilfield drilling data, thereby generating a residual oil exploitation strategy.

[0032] The present invention ensures the basic accuracy of the analysis and monitoring data by obtaining the oilfield exploitation location information and drilling data. This process lays a solid data foundation for subsequent seismic wave reflection signal acquisition and dynamic monitoring. Using standard seismic wave reflection signals for four-dimensional geological structure dynamic monitoring can capture the changes in the underground structure of the oilfield in real time, ensuring the timeliness and accuracy of the monitoring data and providing an important basis for subsequent oil and gas distribution analysis. Based on the dynamic monitoring data of the underground formation structure for oil and gas distribution analysis helps generate a more accurate underground oil and gas distribution map, thereby providing effective visual support and data basis for subsequent exploitation decisions. By calculating the difference between the exploited remaining oil in the underground oil and gas distribution map, the exploitation effect and the remaining resource status can be evaluated in a timely manner, providing data support for adjusting the exploitation strategy. Gridifying the oil and gas distribution map into regions to generate underground oil and gas residual area grids helps with refined management and regional division, enhancing the spatial management ability of underground resources. Analysis of the remaining oil flow path can reveal the dynamic distribution and flow trend of oil and gas underground, providing important clues for formulating a scientific exploitation strategy and reducing resource waste. Calculating the oil quantity flow aggregation and marking high-aggregation degree areas can effectively identify priority exploitation areas, improve the exploitation efficiency and economic benefits, and ensure the continuous profitability of the oilfield. Screening the effective areas of the high-aggregation degree areas ensures that the exploitation activities are concentrated in economically viable areas, thus avoiding unnecessary resource waste and environmental damage. The exploitation strategy formulated based on the residual oil distribution law analysis data makes the oilfield development more scientific and reasonable, improving the overall exploitation efficiency. Therefore, the present invention improves the accuracy and practicality of the remaining oil distribution law analysis through dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification, and systematic exploitation strategy construction.

[0033] In the embodiment of the present invention, reference is made to Figure 1 As described, it is a schematic diagram of the step flow of a method for analyzing the remaining oil distribution law of the present invention. In this example, the method for analyzing the remaining oil distribution law includes the following steps: Step S1: Obtain the oilfield exploitation location information data and the oilfield drilling data; collect seismic wave reflection signals according to the oilfield exploitation location information data to obtain standard seismic wave reflection signals; use the oilfield drilling data to conduct four-dimensional geological structure dynamic monitoring on the standard seismic wave reflection signals, so as to generate the oilfield underground formation structure dynamic monitoring data; In the embodiment of the present invention, the oilfield exploitation location information data is obtained from an oilfield management system or a geological survey institution, including information such as the coordinates, exploitation depth, and oil and gas types of each well position. Collect relevant oilfield drilling data, including drilling logs, formation descriptions, rock physical properties, historical exploitation data, etc. Ensure that the obtained data is in a consistent format (such as CSV, Excel, or database) for subsequent data processing and analysis. Develop a seismic exploration plan according to the exploitation location information data, including selecting appropriate exploration methods (such as reflection seismology, source type, receiver layout, etc.). Determine the acquisition parameters, such as sampling frequency, source excitation method, etc., to ensure signal quality. Conduct on-site exploration, use a source (such as an explosion, a heavy hammer, or a vibrator) to excite seismic waves, and use seismic receivers (such as seismographs, sensors) to collect reflection signals. The collected signal data includes time-series seismic wave reflection signals. Conduct preliminary processing on the collected seismic wave reflection signals, such as noise removal, gain adjustment, and time correction, to obtain standardized seismic wave reflection signals. Combine the standard seismic wave reflection signals with the oilfield drilling data and adopt four-dimensional geological structure dynamic monitoring technology. This process includes analyzing multiple dimensions such as time, space, depth, and geological characteristics. Establish a dynamic monitoring model by combining the seismic wave reflection signals and the drilling data. Analyze the changes in the underground structure by using the time difference and amplitude changes of the reflected waves, generate time-series seismic wave reflection data, and compare it with historical data to identify the dynamic changes of the formation. According to the analysis results, generate the oilfield underground formation structure dynamic monitoring data, including information such as formation depth, structural changes, and fluid movement. These data can provide an important basis for subsequent oil and gas exploration and exploitation.

[0034] Step S2: Conduct underground oil and gas distribution analysis according to the oilfield underground formation structure dynamic monitoring data to generate an underground oil and gas distribution map; calculate the difference in remaining oil for exploitation on the underground oil and gas distribution map to generate remaining oil exploitation data; perform regional grid division on the underground oil and gas distribution map based on the remaining oil exploitation data to generate an underground oil and gas residual area grid; conduct an analysis of the remaining oil flow path on the underground oil and gas residual area grid to generate remaining oil grid flow path data; In the embodiments of the present invention, by collecting and sorting out the dynamic monitoring data of the underground formation structure of the oilfield, the integrity and accuracy of the data are ensured, including parameters such as formation thickness, lithology, porosity, and saturation. Using the formation monitoring data, an underground oil and gas distribution model is constructed through 3D modeling software (such as Petrel or GeoGraphix). The geological statistics method (such as Kriging interpolation method) is applied to predict the oil and gas distribution, and a preliminary underground oil and gas distribution map is generated. The underground oil and gas distribution model is analyzed to identify the location, distribution range, and characteristics of the oil and gas reservoirs, and finally a detailed underground oil and gas distribution map is generated. According to the historical production data and the existing underground oil and gas distribution map, the remaining oil and gas production volume in the oilfield is calculated. The formula is: R = S - E; where, RRR is the remaining oil and gas volume, SSS is the total oil and gas storage volume, and EEE is the produced oil and gas volume. After the calculation is completed, the remaining oil and gas data are sorted into a database for subsequent analysis and decision-making. The remaining oil and gas production data are combined with the underground oil and gas distribution map for regional grid processing. According to the set grid size (such as 100m x 100m), the underground oil and gas distribution map is divided into multiple grid units. In each grid unit, information such as oil and gas storage volume, production volume, and remaining volume is recorded to generate underground oil and gas residual area grid data. Based on the underground oil and gas residual area grid data, a remaining oil flow path analysis model is established using the principles of fluid dynamics. Consider influencing factors such as permeability, porosity, and oil and gas flow resistance. A numerical simulation software (such as COMSOL Multiphysics or Ansys Fluent) is used to simulate the flow path and calculate the flow trajectory of the remaining oil underground. The flow path data are recorded to identify the main oil and gas flow channels and stagnant areas. The simulation results are summarized to form remaining oil grid flow path data, and the oil and gas flow path map is visually represented for subsequent analysis and decision-making.

[0035] Step S3: Adjacent grid splicing is performed on the underground oil and gas residual area grid according to the remaining oil grid flow path data to generate an underground oil and gas residual area flow splicing grid; oil volume aggregation calculation is performed on the underground oil and gas residual area flow splicing grid to generate an oil volume flow aggregation amount; high aggregation degree areas are marked on the underground oil and gas residual area flow splicing grid through the oil volume flow aggregation amount, thereby generating high aggregation degree areas of oil volume flow; In the embodiments of the present invention, adjacent grid cells are identified based on the remaining oil grid flow path data. There is flow path connectivity between these grid cells, which is suitable for splicing. The grid splicing algorithm (such as Delaunay triangulation method or Voronoi diagram method) is used to splice adjacent grids. Ensure that the continuity of the flow path is maintained during the splicing process. The spliced grid is marked as the "underground oil and gas residual area flow splicing grid". After completion of the splicing, a new grid data structure is generated, which contains comprehensive information of the spliced area, including oil and gas properties, flow paths, etc. The following formula is used to calculate the oil flow aggregation amount: In the formula, represents the flowing oil volume, represents the oil density, represents the acceleration due to gravity, represents the flow cross-sectional area, represents the flow height, represents the viscosity of the oil; for each flow splicing grid, the oil volume aggregation calculation is performed using the oil and gas property data (such as density, flow cross-section, and height). The aggregation amount data of all flow splicing grids is summarized to obtain the overall oil flow aggregation amount. The calculation results are organized into a data set for subsequent analysis and decision-making use. A threshold value of the oil flow aggregation amount is preset, and a reasonable threshold value is set according to historical data and expert experience to identify the high aggregation degree area. The oil flow aggregation amount is compared with the preset aggregation amount threshold: when the oil flow aggregation amount is greater than or equal to the aggregation amount threshold, the splicing grid is marked as the "high aggregation degree area". The grid information of the marked high aggregation degree area is recorded to form a new data set. Finally, the "oil flow high aggregation degree area" data is generated for subsequent development, management, and monitoring use.

[0036] Step S4: Screen the effective area of the oil flow high aggregation degree area to obtain the residual oil high aggregation degree effective area data; analyze the remaining oil distribution law of the residual oil high aggregation degree effective area data to generate the remaining oil distribution law analysis data; construct the residual oil exploitation strategy for the oilfield drilling data based on the remaining oil distribution law analysis data, so as to generate the residual oil exploitation strategy.

[0037] In the embodiments of the present invention, by relying on the data of the high-aggregation regions of oil flow, the high-aggregation regions of residual oil are first screened out. Each region is evaluated to ensure that it meets certain effectiveness criteria, such as: the connectivity of the oil and gas flow path. Whether the aggregated oil volume reaches the minimum requirement for economic exploitation. The regions that meet the effectiveness criteria are marked as "effective regions of high-aggregation of residual oil", and the relevant attribute data are recorded for subsequent analysis. Integrate the geological characteristics, oil and gas characteristics and other relevant data of the effective regions to form a "dataset of effective regions of high-aggregation of residual oil". Extract the residual oil characteristics of the effective regions of high-aggregation of residual oil to obtain a dataset containing residual oil characteristics (such as distribution pattern, concentration, etc.). Divide the residual oil characteristic data of the effective regions of high-aggregation into a model training set and a model test set for subsequent model construction and verification. Use machine learning methods such as decision tree algorithms to train the model training set to generate a prediction pre-model of the residual oil distribution law. Optimize and iterate the prediction pre-model according to the model test set to form a more accurate prediction model of the residual oil distribution law. Input the data of the effective regions of high-aggregation of residual oil into the prediction model of the residual oil distribution law for analysis to generate "analysis data of the residual oil distribution law", providing a deep understanding of the distribution and dynamic characteristics of the residual oil and gas. Based on the generated "analysis data of the residual oil distribution law", analyze the exploitation potential and economic benefits of each region to provide a basis for formulating the residual oil exploitation strategy. Combine the oilfield drilling data to evaluate the current exploitation technologies and equipment, and formulate an optimized residual oil exploitation strategy according to the residual oil distribution law. It mainly includes: the selection and improvement suggestions of exploitation technologies, the optimized layout of drilling positions, the exploitation time arrangement and resource allocation strategy. Simulate and evaluate the formulated exploitation strategy to ensure its feasibility in actual operation and make appropriate adjustments according to the feedback.

[0038] Preferably, step S1 includes the following steps: Step S11: Obtain the data of the oilfield exploitation location information and the oilfield drilling data; Step S12: Emit seismic waves according to the oilfield exploitation location information data and record synchronously to obtain seismic wave reflection signals; perform signal preprocessing on the seismic wave reflection signals to generate standard seismic wave reflection signals, where the signal preprocessing includes signal denoising, signal normalization, and signal detrending; Step S13: Use the oilfield drilling data to perform three-dimensional modeling of the underground formation of the oilfield on the standard seismic wave reflection signals to generate three-dimensional modeling data of the underground formation of the oilfield; Step S14: Perform four-dimensional dynamic monitoring of the geological structure of the three-dimensional modeling data of the underground formation of the oilfield to generate dynamic monitoring data of the geological structure of the underground formation of the oilfield.

[0039] In the embodiments of the present invention, the exploitation location information of the oilfield is obtained by using Geographic Information System (GIS) and remote sensing technology, including geographical coordinates, topographic features, etc. Historical drilling records are obtained from relevant databases or oilfield management systems, including important geological information such as drilling depth, rock type, porosity, permeability, etc. The above data are integrated into a unified database for subsequent analysis and modeling. According to the exploitation location information of the oilfield, seismic waves are emitted in the oilfield area (such as using a seismic source). The emission frequency and timing are determined to ensure that the required underground layers can be covered. High-sensitivity receivers are used to synchronously record the seismic wave reflection signals and capture the signals reflected from the underground layers. Signal processing techniques (such as wavelet transform) are applied to remove background noise. The signals are standardized so that their amplitudes are within a unified range for subsequent analysis. The long-term trends in the signals are removed to highlight the short-term change characteristics. Using the oilfield drilling data, the preprocessed standard seismic wave reflection signals are analyzed to extract characteristic parameters. Inversion algorithms (such as wave inversion method) are used to convert the seismic wave data into underground structure information. Combining the known drilling data, a three-dimensional model of the underground strata of the oilfield is established to display the rock characteristics and distributions of different layers. The three-dimensional model data are output, including formation thickness, physical properties (such as density, velocity, etc.) and spatial positions. The key geological parameters to be monitored are determined, such as formation deformation, fluid flow conditions, etc. The three-dimensional modeling data are combined with the time dimension to establish a dynamic monitoring model. Time series data are used to monitor the formation changes in real time, and the reflection signals at different time nodes are collected. Data mining and analysis tools are used to extract the trends and anomalies in the dynamic monitoring data. The dynamic changes of the underground strata are displayed through three-dimensional visualization software to provide decision support.

[0040] Preferably, step S13 includes the following steps: Perform well logging velocity analysis on the oilfield drilling data to generate oilfield well logging velocity data; perform seismic velocity inversion on the standard seismic wave reflection signals according to the oilfield well logging velocity data to generate seismic wave velocity data in the oilfield area; pick up the travel times of the reflected waves from the standard seismic wave reflection signals to generate seismic reflection wave trend point data; perform depth linear conversion on the seismic reflection wave trend point data and the seismic wave velocity data in the oilfield area to generate geological layer depth information data; Perform Fourier transform on the standard seismic wave reflection signals to generate seismic wave reflection spectrum data; identify the reflection interfaces according to the geological layer depth information data for the seismic wave reflection spectrum data to obtain underground layer geological interface data; Extract formation attribute information data from the underground layer geological interface data to obtain formation attribute information data; perform layer modeling based on the reflection interface on the underground layer geological interface data to generate three-dimensional formation structure framework data; perform initial formation attribute assignment on the three-dimensional formation structure framework data through the formation attribute information data to generate three-dimensional modeling data of the underground strata of the oilfield.

[0041] In the embodiments of the present invention, by collecting oilfield drilling data, including drilling records, porosity, permeability and other information. Determine the logging tool type and logging method (such as resistivity, acoustic, density logging, etc.). Use logging data analysis software (such as Geographix, Petrel, etc.) to perform velocity analysis on the logging data, calculate the acoustic velocity at different depth levels (for example, using acoustic logging data), generate oilfield logging velocity data, and record the acoustic velocity value and its depth of each formation. Select a velocity inversion algorithm suitable for the oilfield (such as the least squares inversion method, the inversion method based on genetic algorithm). Use the oilfield logging velocity data to invert the standard seismic wave reflection signal, calculate the seismic wave velocity in the oilfield area, generate oilfield area seismic wave velocity data, and provide the seismic wave propagation velocity information of each formation. Analyze the standard seismic wave reflection signal to identify the start and end times of each reflected wave. Record the travel time information of each reflected wave to generate seismic reflection wave trend point data, including the time domain position and depth information of the reflected wave. Using the reflected wave trend point data and the oilfield area seismic wave velocity data, adopt the depth linear conversion algorithm to convert the travel time data into depth information, generate geological layer depth information data, and provide the depth and geological distribution of each formation. Perform Fourier transform on the standard seismic wave reflection signal to convert it into a frequency domain signal to extract frequency components, generate seismic wave reflection spectrum data, and record the energy distribution of reflected waves at different frequencies. Based on the geological layer depth information data and the seismic wave reflection spectrum data, use the adaptive threshold method or machine learning algorithm to identify the reflection interface, obtain the underground layer geological interface data, and record the reflection interface position and attribute information of each formation. Analyze the underground layer geological interface data to extract the attribute information of each formation, such as density, porosity, etc., to obtain the formation attribute information data and form the physical attribute record of each formation. Based on the underground layer geological interface data, establish a formation model using the reflection interface method, define the geometric shape and physical attributes of each layer, generate three-dimensional formation structure framework data, and describe the three-dimensional formation structure of the entire oilfield. According to the formation attribute information data, initially assign values to each layer of the three-dimensional formation structure framework data to ensure that each layer has physical attributes, generate three-dimensional modeling data of the oilfield underground formation, form a complete underground formation model, and be used for subsequent analysis and development.

[0042] Preferably, step S14 includes the following steps: Step S141: Confirm the time reference point for the three-dimensional modeling data of the oilfield underground formation to obtain the monitoring time node data; based on the monitoring node time data, collect repeated seismic exploration data for the three-dimensional modeling data of the oilfield underground formation to obtain the seismic exploration time series dataset; Step S142: Conduct seismic data difference analysis on the seismic exploration time series dataset to generate a time-difference formation change profile; conduct formation attribute contrast analysis on the time-difference formation change profile to generate a formation attribute change distribution map; Step S143: Dynamically update the dynamic geological data of the three-dimensional modeling data of the underground formation of the oilfield through the formation attribute change map to generate dynamic monitoring data of the underground formation structure of the oilfield, where the dynamic geological data update includes dynamic update of the structural form and dynamic update of the attributes.

[0043] In the embodiment of the present invention, by determining the monitored time nodes, such as the initial time of seismic exploration, the time interval of each exploration, and the subsequent update time points. Using project management tools and time series analysis methods, generate monitored time node data and record the exact time of each node. Based on the monitored node time data, design a repeated seismic exploration plan, including the parameters and equipment configuration of the emission source and the recorded received signal. Execute the repeated seismic exploration operation to collect the seismic exploration time series dataset, including the seismic wave reflection signals at different time nodes. Compare and analyze the seismic exploration time series datasets at different time nodes to identify the changes in the seismic wave reflection signals. Calculate the time-difference formation change and extract the signal differences caused by the changes in the underground formation. Use seismic data processing software (such as Seismic Unix, Petrel, etc.) to draw a time-difference formation change profile to visually display the formation changes between different time nodes. Mark the obvious change areas on the profile to assist subsequent analysis. Compare and analyze the formation attributes in the time-difference formation change profile to determine the physical attribute changes (such as density, porosity, etc.) of each formation, generate a formation attribute change distribution map, record the attribute changes of each horizon, and provide visual data support. Based on the formation attribute change distribution map, dynamically update the three-dimensional modeling data of the underground formation of the oilfield. The update content includes: adjusting the geometry of the formation according to the seismic exploration time series data to reflect the actual formation changes (such as subsidence, uplift, etc.). According to the formation attribute change distribution map, update the physical attributes of each horizon in the three-dimensional model, such as updating parameters such as porosity, permeability, and fluid saturation. Integrate the updated data to form dynamic monitoring data of the underground formation structure of the oilfield, which is convenient for subsequent analysis, decision-making, and management. Provide a visual display so that the oilfield management team can monitor the underground structure changes in real time and make corresponding resource management and development plan adjustments.

[0044] As an example of the present invention, refer to Figure 2 shown, in this example, the step S2 includes: Step S21: Analyze the underground oil and gas distribution based on the dynamic monitoring data of the underground formation structure of the oilfield to generate an underground oil and gas distribution map; calculate the total oil and gas storage volume for the underground oil and gas distribution map to obtain the total oil and gas storage volume data; calculate the oil and gas production volume for the underground oil and gas distribution map based on the total oil and gas storage volume data to obtain the oil and gas production volume data. Step S22: Calculate the data difference between the total oil and gas storage volume data and the oil and gas production volume data to generate the remaining oil and gas production data; compare the remaining oil and gas production data with the preset residual oil and gas reserve threshold. When the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, regional grid division is performed on the underground oil and gas distribution map based on the remaining oil and gas production data to generate an underground oil and gas residual area grid. Step S23: Analyze the regional geological hardness of the underground oil and gas residual area grid through the dynamic monitoring data of the underground formation structure of the oilfield to generate grid regional geological hardness data; use the grid regional geological hardness data to analyze the remaining oil flow path of the underground oil and gas residual area grid to generate remaining oil grid flow path data.

[0045] In the embodiments of the present invention, by collecting dynamic monitoring data of the underground formation structure of the oilfield, including structural morphology, attribute changes, and other relevant geological data. Using geological modeling software (such as Petrel, GeoGraphix, etc.), the dynamic monitoring data of the structure is integrated with the oil and gas distribution model. Geostatistical methods (such as Kriging method, inverse distance weighting method) are used to analyze the oil and gas distribution, and an underground oil and gas distribution map is generated. The main oil and gas storage areas, potential oil and gas migration channels, and enrichment areas are identified. According to the oil and gas distribution map, the total oil and gas storage in each area is calculated using the physical properties of the formation (such as porosity, permeability, saturation, etc.). The volume method or material balance method is used for the calculation of the storage volume to obtain the total oil and gas storage data. According to the total oil and gas storage data, combined with the production plan and historical production data, the future oil and gas production is estimated to generate oil and gas production data, and the estimated production volume at each time node is recorded. The difference between the total oil and gas storage data and the oil and gas production data is calculated to generate the remaining oil and gas production data. The specific value of the remaining oil and gas production data is determined, and the remaining recoverable oil and gas volume is recorded. The remaining oil and gas production data is compared with the preset residual oil and gas reserve threshold. When the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, the next step is carried out. Based on the remaining oil and gas production data, the underground oil and gas distribution map is subjected to regional grid processing, different oil and gas storage grid areas are divided, and an underground oil and gas residual area grid is generated, providing basic data for subsequent analysis. Through the dynamic monitoring data of the underground formation structure of the oilfield, the geological hardness of different areas is analyzed, and corresponding geological hardness indicators (such as rock strength, stress value, etc.) are used. A geomechanics model is used for regional geological hardness analysis to generate grid area geological hardness data. According to the grid area geological hardness data, an oil and gas flow path model is established, considering the geological characteristics, pressure difference, and fluid mechanics principles of each area, to generate remaining oil grid flow path data, and the oil and gas flow path and potential flow channels in each grid area are recorded.

[0046] Preferably, when comparing the remaining oil and gas production data with the preset residual oil and gas reserve threshold, and when the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, the regional grid processing of the underground oil and gas distribution map based on the remaining oil and gas production data includes: When comparing the remaining oil and gas production data with the preset residual oil and gas reserve threshold, and when the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, an initial grid cell division is performed on the underground oil and gas distribution map based on the remaining oil and gas production data to generate initial grid cell data, where the initial grid cell data includes formation attributes and oil and gas attributes; Calculate the grid occupancy ratios of formation properties and oil-gas properties to obtain formation-oil-gas grid occupancy ratio data; balance the property occupancy ratios of the initial grid cell data according to the formation-oil-gas grid occupancy ratio data to generate internal adjustment data for the grid cells; perform oil-gas dynamic displacement analysis on the oil-gas properties to generate oil-gas dynamic displacement data; Perform grid boundary adaptive adjustment on the initial grid cell data according to the oil-gas dynamic displacement data to generate external adjustment data for the grid cells; allocate grid numbers to the initial grid cell data based on the internal adjustment data and the external adjustment data for the grid cells to generate a grid for the remaining underground oil-gas region.

[0047] In an embodiment of the present invention, the remaining oil-gas production data is compared one by one with a preset remaining oil-gas reserve threshold to determine a qualified region, that is, when the remaining oil-gas production data is less than or equal to the remaining oil-gas reserve threshold, proceed to the subsequent steps. According to the remaining oil-gas production data, perform initial grid cell division on the underground oil-gas distribution map. The division basis of the initial grid cells can adopt a uniform grid division method (such as square or hexagonal grids) to ensure coverage of the entire oil-gas distribution region and generate initial grid cell data, including the formation properties (such as porosity, permeability, etc.) and oil-gas properties (such as oil-gas reserves, saturation, etc.) of each grid cell. Calculate the occupancy ratios of the formation properties and oil-gas properties in each initial grid cell to generate formation-oil-gas grid occupancy ratio data. Use a data analysis tool to analyze the occupancy ratios of different formation and oil-gas characteristics in each grid cell to ensure comprehensive evaluation. Balance the property occupancy ratios of the initial grid cell data according to the formation-oil-gas grid occupancy ratio data. By adjusting the formation properties and oil-gas properties in the grid cells, ensure a reasonable ratio between the two to generate internal adjustment data for the grid cells. Perform dynamic displacement analysis on the oil-gas properties, considering the flow and diffusion processes of oil-gas in the formation. Adopt a hydrodynamic model or numerical simulation method to generate oil-gas dynamic displacement data and record the position changes of oil-gas in each grid cell. Perform grid boundary adaptive adjustment on the initial grid cell data according to the oil-gas dynamic displacement data. Considering the influence of dynamic displacement, adjust the boundaries of the grid cells to make them more conform to the actual oil-gas distribution situation to generate external adjustment data for the grid cells. Based on the internal adjustment data and the external adjustment data for the grid cells, allocate numbers to the initial grid cells. Use a numbering system (such as area number + cell number) to ensure that each grid cell has a unique identifier, facilitating subsequent data management and analysis. Combine the adjusted initial grid cell data with the numbers to generate a complete grid for the remaining underground oil-gas region. Record the property information and spatial position information of each grid cell to form the final regional grid data, providing a basis for subsequent oil-gas production decision-making and dynamic monitoring.

[0048] Preferably, step S23 includes the following steps: Step S231: Calculate the geological hardness of the underground oil and gas residual area grid based on the dynamic monitoring data of the underground formation structure of the oilfield, and generate the geological hardness data of the grid area. The formula for calculating the geological hardness is as follows:

[0049] In the formula, represents the geological hardness, represents the elastic modulus, represents the rock density; Step S232: Screen the low-hardness grids of the underground oil and gas residual area grid according to the geological hardness data of the grid area to obtain the low-hardness screened grids. Analyze the geological porosity of the low-hardness screened grids to generate the geological porosity data of the low-hardness grids; Step S233: Simulate the remaining oil and gas flow paths of the underground oil and gas residual area grid through the geological porosity data of the low-hardness grids to generate the oil and gas flow path simulation data; Step S234: Visualize the oil and gas flow path simulation data to generate the remaining oil grid flow path data.

[0050] In the embodiment of the present invention, the geological hardness is calculated through a formula, and the rock density and elastic modulus data of different grid areas are obtained through the dynamic monitoring data of the underground formation structure of the oilfield. The geological hardness of each grid unit is calculated using the above formula to generate the geological hardness data of the grid area. According to the calculated geological hardness data of the grid area, a threshold for low hardness is set (for example: H < Hthreshold). The grids with hardness lower than this threshold are screened out to obtain the low-hardness screened grids. The geological porosity analysis is carried out on the low-hardness screened grids to obtain the porosity data (such as obtained through well logging data or experimental analysis), generate the geological porosity data of the low-hardness grids, and record the porosity characteristics of each low-hardness grid. Based on the geological porosity data of the low-hardness grids, the remaining oil and gas flow paths are simulated using a numerical simulation method (such as a computational fluid dynamics model). Considering the permeability characteristics of the low-hardness area, the oil and gas flow path simulation data is generated, and the flow of oil and gas between each low-hardness grid is recorded. The oil and gas flow path simulation data is visualized, and the simulation results are presented using graphical software (such as MATLAB, Matplotlib in Python, or other geological modeling software) to generate the remaining oil grid flow path data, showing the flow trend and distribution characteristics of oil and gas between the low-hardness grids.

[0051] As an example of the present invention, refer to Figure 3 shown. In this example, the step S3 includes: Step S31: Mark the flow boundary points of the underground oil and gas residual area grid according to the remaining oil grid flow path data to obtain the oil and gas flow boundary points of the regional grid; use the oil and gas flow boundary points of the regional grid to splice adjacent grids of the underground oil and gas residual area grid to generate the underground oil and gas residual area flow splicing grid; Step S32: Connect the remaining oil grid flow path data through the underground oil and gas residual area flow splicing grid to generate the remaining oil flow path network; calculate the oil volume aggregation of the remaining oil flow path network according to the gravitational acceleration to generate the oil volume flow aggregation; the formula for the oil volume aggregation calculation is as follows:

[0052] In the formula, represents the flowing oil volume, represents the oil density, represents the gravitational acceleration, represents the flow cross-sectional area, represents the flow height, represents the oil viscosity; Step S33: Return the oil volume flow aggregation to step S22 and compare it with the preset residual oil and gas reserve threshold again. When the oil volume flow aggregation is greater than or equal to the preset residual oil and gas reserve threshold, mark the high-aggregation area for the underground oil and gas residual area flow splicing grid based on the oil volume flow aggregation, so as to generate the oil volume flow high-aggregation area.

[0053] In the embodiment of the present invention, by identifying and marking the flow boundary points according to the remaining oil grid flow path data. The flow boundary points are the starting and ending points of the oil and gas flow in the underground oil and gas residual area grid, usually the turning points or convergence points of the flow path, generate the oil and gas flow boundary point data of the regional grid, and record the coordinates of each boundary point and its corresponding oil and gas flow state. Use the oil and gas flow boundary points of the regional grid to splice adjacent grids of the underground oil and gas residual area grid. According to the position of the flow boundary points, merge adjacent grids to form the underground oil and gas residual area flow splicing grid. This process helps to simplify the model and improve the efficiency of subsequent analysis. Connect the remaining oil grid flow path data through the underground oil and gas residual area flow splicing grid to form the overall network of oil and gas flow, generate the remaining oil flow path network data, and record the characteristics of each connection path. Calculate the oil volume aggregation of the remaining oil flow path network according to the gravitational acceleration, using the formula: , in the formula, represents the flowing oil volume, represents the oil density, represents the gravitational acceleration, represents the flow cross-sectional area, represents the flow height, It is expressed as the viscosity of the oil; through the above formula, the flowing oil volume of each path is calculated, and the oil volume flowing aggregation data is formed by summarization. The calculated oil volume flowing aggregation is returned to step S22 and compared again with the preset residual oil and gas reserve threshold. If the oil volume flowing aggregation is greater than or equal to the preset residual oil and gas reserve threshold, proceed to the next step. Based on the oil volume flowing aggregation, the high-aggregation regions of the underground oil and gas residual area flow splicing grid are marked. During the marking process, the boundaries, positions and their corresponding flowing oil volume characteristics of the high-aggregation regions are recorded, and the oil volume flowing high-aggregation region data is generated.

[0054] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes: Step S41: Monitor the residual oil in the high-aggregation regions of the oil volume flowing, and generate the residual oil monitoring data for the high-aggregation regions; Step S42: Screen the effective regions of the high-aggregation regions of the oil volume flowing according to the residual oil monitoring data for the high-aggregation regions, and obtain the residual oil high-aggregation effective region data; Step S43: Analyze the remaining oil distribution law of the residual oil high-aggregation effective region data, and generate the remaining oil distribution law analysis data; construct the residual oil exploitation strategy for the oilfield drilling data based on the remaining oil distribution law analysis data, so as to generate the residual oil exploitation strategy.

[0055] In the embodiments of the present invention, by arranging monitoring devices, such as ground sensors and underground monitoring wells, in areas with high oil flow aggregation, the concentration and distribution information of residual oil in the area are collected in real time. The monitoring devices should have data acquisition, storage, and transmission functions to ensure efficient data processing. Regularly collect the residual oil monitoring data in the area, including information such as residual oil concentration, distribution location, and time series, generate the residual oil monitoring data for the high-aggregation area, record the residual oil characteristics and change trends of each monitoring point, and provide basic data for subsequent analysis. According to the residual oil monitoring data of the high-aggregation area, apply data analysis algorithms (such as clustering analysis, statistical analysis, etc.) to identify areas with high residual oil content. Set a screening threshold, and determine the residual oil concentration standard for the effective area based on historical data and real-time monitoring data. Through the data analysis results, screen the effective areas in the areas with high oil flow aggregation to obtain the data of the effective areas with high residual oil aggregation. Record the boundaries, locations, residual oil concentrations, and other relevant attributes of the screened effective areas to provide a basis for subsequent analysis of the remaining oil distribution. Conduct a detailed analysis of the data of the effective areas with high residual oil aggregation, including spatial distribution, depth distribution, and time variation, etc. Apply mathematical models (such as multiple regression analysis, spatial interpolation method, etc.) to extract the residual oil distribution law and generate the analysis data of the residual oil distribution law. Based on the analysis data of the residual oil distribution law, combined with the oilfield drilling data, construct a residual oil recovery strategy. The strategy should consider the best recovery method, recovery time arrangement, equipment selection, and resource allocation to maximize the recovery efficiency of residual oil and form a complete residual oil recovery strategy document.

[0056] Preferably, the analysis of the remaining oil distribution law for the data of the effective areas with high residual oil aggregation includes: Extract the residual oil characteristics of the high-aggregation effective area from the data of the effective areas with high residual oil aggregation to obtain the residual oil characteristic data of the high-aggregation effective area; divide the residual oil characteristic data of the high-aggregation effective area into data sets to generate a model training set and a model test set; train the model training set through a decision tree algorithm to generate a prediction pre-model of the residual oil distribution law. Optimize and iterate the prediction pre-model of the residual oil distribution law according to the model test set to generate a prediction model of the residual oil distribution law; import the data of the effective areas with high residual oil aggregation into the prediction model of the residual oil distribution law for the analysis of the residual oil distribution law, and thus generate the analysis data of the residual oil distribution law.

[0057] In the embodiments of the present invention, by cleaning the data of the effective area with high residual oil concentration, invalid data and outliers are removed to ensure data quality. The data is standardized to facilitate subsequent analysis and model training. Data mining techniques are applied to extract key features from the effective area data, such as the residual oil concentration distribution, geological properties (such as porosity, permeability), historical production records, and oilfield formation structure, to generate residual oil characteristic data for the effective area with high concentration, for subsequent model training. According to the extracted residual oil characteristic data, the data is divided into a model training set and a model test set according to a certain ratio (such as 70% training set, 30% test set). Ensure that the characteristic distributions of the training set and the test set are as consistent as possible to improve the generalization ability of the model. Save the generated model training set and model test set to prepare for subsequent model training and evaluation. Determine to use the decision tree algorithm for model training because it has good interpretability and the ability to handle non-linear relationships. Configure the parameters of the decision tree, such as the maximum depth of the tree, the minimum number of samples for splitting, etc., to prevent overfitting. Use the model training set to train the decision tree algorithm to generate a pre-model for predicting the residual oil distribution law. Record the model performance indicators (such as accuracy, recall, etc.) during the training process for subsequent optimization. Use the model test set to evaluate the pre-model for predicting the residual oil distribution law and calculate the performance indicators of the model (such as accuracy, F1 score, etc.). Adjust the parameters of the decision tree model according to the evaluation results for model optimization iteration. The cross-validation method can be used to ensure the stability of the model. Repeat the training and evaluation process until satisfactory model performance indicators are achieved to generate the final residual oil distribution law prediction model. Import the data of the effective area with high residual oil concentration into the finally generated residual oil distribution law prediction model. Use the model to analyze the residual oil distribution law, obtain the prediction results of the residual oil distribution in the area, and generate residual oil distribution law analysis data, including residual oil distribution maps, concentration predictions, and other key features. Organize the analysis results into a report and provide it to the oilfield management department to support subsequent production decisions and strategy formulation.

[0058] The beneficial effects of the present invention are as follows: By obtaining the oilfield exploitation location information data and the oilfield drilling data, the basic integrity of the data is ensured, providing an accurate geographical and technical background for subsequent analysis. By collecting standard seismic wave reflection signals, the formation structure of the oilfield can be clearly identified, promoting more accurate underground imaging. Real-time dynamic monitoring of the geological structure changes in the oilfield helps to adjust the exploitation strategy and optimize the resource allocation in a timely manner. The underground oil and gas distribution map generated based on the dynamic monitoring data can provide the spatial distribution information of oil and gas, helping to identify the oil and gas enrichment areas. Calculating the exploitation data of the remaining oil and gas helps to evaluate the development potential and economic benefits of the oilfield. Dividing the underground oil and gas distribution map into regional grids can refine the analysis, making the subsequent flow path analysis more accurate. Defining the flow path of the remaining oil helps to optimize the exploitation strategy and improve the recovery rate of the remaining oil. By splicing adjacent grids, the data of different regions can be integrated to form a more comprehensive flow splicing grid of the oil and gas residual area. Accurately calculating the oil flow aggregation amount provides a basis for identifying the high-aggregation areas in the subsequent process. Marking the high-aggregation areas helps to quickly locate the areas with the most development value and improve the exploitation efficiency. By screening the high-aggregation areas of the oil flow, the efficiency of the development resources is ensured, avoiding ineffective exploitation. Analyzing the effective high-aggregation areas can reveal the distribution characteristics of the remaining oil and optimize the subsequent exploitation strategy. The residual oil exploitation strategy formulated based on the analysis data makes the exploitation process more scientific and systematic. Therefore, the present invention improves the accuracy and practicality of the analysis of the remaining oil distribution law through dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification, and systematic exploitation strategy construction.

[0059] 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, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0060] The above are 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 widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for analyzing the distribution law of residual oil, characterized in that: The following steps are involved: Step S1: Acquire oilfield production location information data and oilfield drilling data; collect seismic wave reflection signals according to the oilfield production location information data to obtain standard seismic wave reflection signals; use the oilfield drilling data to perform four-dimensional geological structure dynamic monitoring on the standard seismic wave reflection signals, thereby generating oilfield underground formation structure dynamic monitoring data; Step S2: Analyze the underground oil and gas distribution according to the dynamic monitoring data of the underground stratum structure of the oil field to generate an underground oil and gas distribution map; Calculate the difference of remaining oil and gas produced on the underground oil and gas distribution map to generate remaining oil and gas production data; Based on the remaining oil and gas production data, the underground oil and gas distribution map is regionally gridded to generate the underground oil and gas residual regional grid; the remaining oil flow path analysis is performed on the underground oil and gas residual regional grid to generate the remaining oil grid flow path data; Step S3: splicing adjacent grids of the underground oil and gas residual area grid according to the residual oil grid flow path data to generate a spliced ​​grid of the underground oil and gas residual area flow; The oil accumulation calculation is performed on the flow splicing grid of the underground oil and gas residual area to generate the oil flow accumulation; The high-concentration area of ​​the flow splicing grid in the underground oil and gas residual area is marked by the oil flow concentration, so as to generate the high-concentration area of ​​oil flow; Step S4: Screen the effective areas of the oil flow high concentration area to obtain the residual oil high concentration effective area data; analyze the residual oil distribution law of the residual oil high concentration effective area data to generate residual oil distribution law analysis data; construct the residual oil recovery strategy for the oilfield drilling data based on the residual oil distribution law analysis data, thereby generating the residual oil recovery strategy.

2. The residual oil distribution law analysis method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire oilfield mining location information data and oilfield drilling information data; Step S12: transmitting seismic waves according to the oilfield production location information data, and synchronously recording to obtain seismic wave reflection signals; performing signal preprocessing on the seismic wave reflection signals to generate standard seismic wave reflection signals, wherein the signal preprocessing includes signal denoising, signal normalization and signal detrending processing; Step S13: using the oilfield drilling data to perform three-dimensional oilfield underground stratum modeling on the standard seismic wave reflection signal to generate three-dimensional modeling data of the oilfield underground stratum; Step S14: Perform four-dimensional geological structure dynamic monitoring on the three-dimensional modeling data of the oil field underground strata, thereby generating dynamic monitoring data of the oil field underground strata structure.

3. The residual oil distribution law analysis method according to claim 2, characterized in that: Step S13 includes the following steps: Perform logging velocity analysis on oilfield drilling data to generate oilfield logging velocity data; perform seismic velocity inversion on standard seismic wave reflection signals based on oilfield logging velocity data to generate oilfield regional seismic wave velocity data; perform reflection wave travel time pick-up on standard seismic wave reflection signals to generate seismic reflection wave trend point data; perform depth linear conversion on seismic reflection wave trend point data and oilfield regional seismic wave velocity data to generate geological layer depth information data; Perform Fourier transform on the standard seismic wave reflection signal to generate seismic wave reflection spectrum data; perform reflection interface identification on the seismic wave reflection spectrum data according to the geological layer depth information data to obtain underground layer geological interface data; Extract stratigraphic attribute information from underground geological interface data to obtain stratigraphic attribute information data; perform reflection interface-based stratigraphic modeling based on underground geological interface data to generate three-dimensional stratigraphic structure framework data; assign initial stratigraphic attributes to the three-dimensional stratigraphic structure framework data through the stratigraphic attribute information data to generate three-dimensional modeling data of underground strata in oil fields.

4. The method for analyzing the residual oil distribution law according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: confirming the time reference point of the three-dimensional modeling data of the oilfield underground stratum to obtain monitoring time node data; repeating the seismic exploration data collection on the three-dimensional modeling data of the oilfield underground stratum based on the monitoring node time data to obtain a seismic exploration time series data set; Step S142: performing seismic data difference analysis on the seismic exploration time series data set to generate a time difference stratigraphic change profile; performing stratigraphic attribute comparison analysis on the time difference stratigraphic change profile to generate a stratigraphic attribute change distribution map; Step S143: Dynamically update the three-dimensional modeling data of the oilfield underground strata through the stratum attribute change diagram to generate dynamic monitoring data of the oilfield underground stratum structure, wherein the dynamic geological data update includes dynamic update of structural morphology and dynamic update of attributes.

5. The residual oil distribution law analysis method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Analyze the underground oil and gas distribution according to the dynamic monitoring data of the underground stratum structure of the oil field to generate an underground oil and gas distribution map; calculate the total amount of oil and gas storage on the underground oil and gas distribution map to obtain total amount of oil and gas storage data; calculate the oil and gas production volume on the underground oil and gas distribution map according to the total amount of oil and gas storage data to obtain oil and gas production volume data; Step S22: performing data difference calculation on the total oil and gas storage data and the oil and gas production data to generate remaining oil and gas production data; comparing the remaining oil and gas production data with a preset residual oil and gas reserve threshold; when the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, regional gridding is performed on the underground oil and gas distribution map based on the remaining oil and gas production data to generate an underground oil and gas residual regional grid; Step S23: Perform regional geological hardness analysis on the underground oil and gas residual area grid using the dynamic monitoring data of the underground stratum structure of the oil field to generate grid regional geological hardness data; perform residual oil flow path analysis on the underground oil and gas residual area grid using the grid regional geological hardness data to generate residual oil grid flow path data.

6. The method for analyzing the residual oil distribution law according to claim 5, characterized in that: The remaining oil and gas production data is compared with the preset residual oil and gas reserve threshold. When the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, the underground oil and gas distribution map is regionally gridded based on the remaining oil and gas production data, including: The remaining oil and gas production data is compared with a preset residual oil and gas reserve threshold. When the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold, the underground oil and gas distribution map is initially divided into grid cells based on the remaining oil and gas production data to generate initial grid cell data, wherein the initial grid cell data includes formation attributes and oil and gas attributes; Calculate the grid proportions of formation attributes and oil and gas attributes to obtain formation-oil and gas grid proportion data; balance the attribute proportions of the initial grid unit data based on the formation-oil and gas grid proportion data to generate grid unit internal adjustment data; perform oil and gas dynamic displacement analysis on oil and gas attributes to generate oil and gas dynamic displacement data; The grid boundary of the initial grid cell data is adaptively adjusted according to the oil and gas dynamic displacement data, thereby generating the grid cell external adjustment data; the grid number is allocated to the initial grid cell data based on the grid cell internal adjustment data and the grid cell external adjustment data, thereby generating the underground oil and gas residual area grid.

7. The method for analyzing the residual oil distribution pattern according to claim 5, characterized in that: Step S23 includes the following steps: Step S231: Calculate the geological hardness of the underground oil and gas residual area grid through the dynamic monitoring data of the underground stratum structure of the oil field to generate the geological hardness data of the grid area; the formula for calculating the geological hardness is as follows: In the formula, Expressed as geological hardness, Expressed as elastic modulus, Expressed as rock density; Step S232: screening the underground oil and gas residual area grids for low hardness grids according to the grid area geological hardness data to obtain low hardness screening grids; performing geological porosity analysis on the low hardness screening grids to generate low hardness grid geological porosity data; Step S233: simulating the remaining oil and gas flow path of the underground oil and gas residual area grid by using the low-hardness grid geological porosity data to generate oil and gas flow path simulation data; Step S234: Perform path visualization on the oil and gas flow path simulation data to generate remaining oil grid flow path data.

8. The method for analyzing the residual oil distribution law according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: marking the flow boundary points of the underground oil and gas residual regional grid according to the residual oil grid flow path data to obtain the regional grid oil and gas flow boundary points; using the regional grid oil and gas flow boundary points to splice the adjacent grids of the underground oil and gas residual regional grid to generate the underground oil and gas residual regional flow splicing grid; Step S32: connect the remaining oil grid flow path data through the underground oil and gas residual area flow splicing grid to generate a remaining oil flow path network; perform oil volume aggregation calculation on the remaining oil flow path network according to gravity acceleration to generate oil volume flow aggregation; wherein the formula for oil volume aggregation calculation is as follows: In the formula, Expressed as the amount of flowing oil, Expressed as oil density, Expressed as the acceleration due to gravity, Expressed as the flow cross-sectional area, Expressed as flow height, Expressed as the viscosity of the oil; Step S33: Return the oil flow accumulation amount to step S22 and compare it with the preset residual oil and gas reserves threshold value again. When the oil flow accumulation amount is greater than or equal to the preset residual oil and gas reserves threshold value, the high-accumulation area of ​​the underground oil and gas residual area flow splicing grid is marked based on the oil flow accumulation amount, thereby generating an oil flow high-accumulation area.

9. The method for analyzing the residual oil distribution law according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing regional residual oil monitoring on the oil flow high concentration area to generate residual oil monitoring data of the high concentration area; Step S42: Screening effective areas of oil flow high concentration areas according to the residual oil monitoring data of high concentration areas to obtain effective area data of residual oil high concentration; Step S43: Analyze the residual oil distribution law of the effective area data with high residual oil concentration to generate residual oil distribution law analysis data; construct a residual oil production strategy for the oilfield drilling data based on the residual oil distribution law analysis data to generate a residual oil production strategy.

10. The method for analyzing the residual oil distribution law according to claim 9, characterized in that: The analysis of the residual oil distribution law of the effective area data with high residual oil concentration includes: Extract regional residual oil characteristics from the effective area data of residual oil with high concentration to obtain residual oil characteristic data of effective area with high concentration; divide the residual oil characteristic data of effective area with high concentration into data sets to generate model training sets and model test sets; train the model training sets through the decision tree algorithm to generate a residual oil distribution law prediction pre-model; The residual oil distribution law prediction pre-model is optimized and iterated according to the model test set to generate the residual oil distribution law prediction model; the effective area data of residual oil high concentration is imported into the residual oil distribution law prediction model to analyze the residual oil distribution law, thereby generating the residual oil distribution law analysis data.

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