A method for analyzing residual oil distribution regularity

Abstract

The present application relates to the technical field of data analysis, and particularly relates to a remaining oil distribution rule analysis method.The method comprises the following steps: obtaining oilfield exploitation position information data and oilfield drilling data; collecting seismic wave reflection signals according to the oilfield exploitation position information data to obtain standard seismic wave reflection signals; performing four-dimensional geological structure dynamic monitoring on the standard seismic wave reflection signals by using the oilfield drilling data, so as to generate oilfield underground formation structure dynamic monitoring data; performing 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; and performing remaining oil and gas exploitation difference calculation on the underground oil and gas distribution map to generate remaining oil and gas exploitation data.The present application improves the accuracy and practicability of the remaining oil distribution rule analysis by means of dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification and systematic exploitation strategy construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a remaining oil distribution rule analysis method. BACKGROUND

[0002] Initially, the study of remaining oil mainly relied on empirical analysis and simple physical models. At this stage, geologists preliminarily understood the formation and evolution of oil reservoirs through the basic knowledge of stratigraphy and sedimentology, but lacked systematic analysis of the distribution rule of remaining oil. With the development of technology, especially after the 1970s, the progress of seismic exploration technology made the identification of stratigraphic structure more accurate. At this time, researchers began to use seismic data to invert the characteristics of oil reservoirs, and then revealed the distribution pattern of remaining oil. At the same time, the application of numerical simulation technology made dynamic reservoir simulation possible, which could 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 can process complex multidimensional data sets to discover the distribution rule of remaining oil hidden in the data. However, the existing technology often lacks a systematic method when analyzing the flow path of remaining oil, and cannot effectively identify the high concentration area of oil flow, which leads to low accuracy and practicability of the analysis of the distribution rule of remaining oil. SUMMARY

[0003] Therefore, it is necessary to provide a remaining oil distribution rule analysis method to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a remaining oil distribution rule analysis method, the method comprising the following steps:

[0005] Step S1: obtaining oilfield exploitation position information data and oilfield drilling data; collecting seismic wave reflection signals according to the oilfield exploitation position information data to obtain standard seismic wave reflection signals; using 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;

[0006] Step S2: performing 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; performing exploitation remaining oil and gas difference calculation on the underground oil and gas distribution map to generate remaining oil and gas exploitation data; performing regional gridding on the underground oil and gas distribution map based on the remaining oil and gas exploitation data to generate an underground oil and gas residual area grid; performing remaining oil flow path analysis on the underground oil and gas residual area grid to generate remaining oil grid flow path data;

[0007] Step S3: According to the remaining oil grid flow path data, the adjacent grid splicing of the underground oil and gas residual area grid is carried out, and the underground oil and gas residual area flow splicing grid is generated; the oil flow aggregation calculation is carried out on the underground oil and gas residual area flow splicing grid, and the oil flow aggregation amount is generated; the underground oil and gas residual area flow splicing grid is marked by the oil flow aggregation amount, and the oil flow high aggregation degree area is generated.

[0008] Step S4: The oil flow high aggregation degree area is screened for effective area, and residual oil high aggregation degree effective area data is obtained; the residual oil distribution rule analysis data is generated by analyzing the residual oil distribution rule of the residual oil high aggregation degree effective area data; and the residual oil exploitation strategy is constructed based on the residual oil distribution rule analysis data and the oilfield drilling data, thereby generating the residual oil exploitation strategy.

[0009] The present application ensures the basic accuracy of the analysis and monitoring data by obtaining the oilfield exploitation position 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 stratum structure, oil and gas distribution analysis can help generate more accurate underground oil and gas distribution maps, thereby providing effective visual support and data basis for subsequent exploitation decisions. By calculating the difference between the exploitation and the remaining oil and gas, the exploitation effect and the remaining resource situation can be evaluated in a timely manner, providing data support for adjusting the exploitation strategy. The oil and gas distribution map is regionally gridded to generate an underground oil and gas residual area grid, which helps fine-grained management and regional division, enhancing the spatial management capability of underground resources. The remaining oil flow path analysis can reveal the dynamic distribution and flow trend of oil and gas underground, providing important clues for developing scientific exploitation strategies and reducing resource waste. The calculation of oil flow aggregation and the marking of high aggregation degree areas can effectively identify priority exploitation areas, improve exploitation efficiency and economic benefits, and ensure the sustainable profitability of oilfields. The effective area screening of high aggregation degree areas ensures that exploitation activities are concentrated in areas with economic benefits, thereby avoiding unnecessary resource waste and environmental damage. The exploitation strategy based on residual oil distribution rule analysis data ensures the sustainable development of oilfield resources, making oilfield development more scientific and reasonable, and improving the overall exploitation efficiency. Therefore, the present application improves the accuracy and practicality of residual oil distribution rule analysis through dynamic monitoring, fine-grained oil and gas distribution analysis, efficient flow path identification, and systematic exploitation strategy construction.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtain oilfield exploitation location information data and oilfield drilling data;

[0012] 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, wherein the signal preprocessing includes signal denoising, signal normalization, and signal detrending processing;

[0013] Step S13: Perform three-dimensional oilfield subsurface formation modeling on the standard seismic wave reflection signals using the oilfield drilling data to generate three-dimensional modeling data of the oilfield subsurface formation;

[0014] Step S14: Perform four-dimensional geological structure dynamic monitoring on the three-dimensional modeling data of the oilfield subsurface formation to generate oilfield subsurface formation structure dynamic monitoring data.

[0015] The present application ensures the accuracy of the data by obtaining oilfield exploitation location information and drilling data, providing reliable basic data for subsequent seismic wave transmission and signal processing. By denoising, normalizing, and detrending the seismic wave reflection signals to generate standardized signals, the accuracy of the data can be significantly improved, which helps to model the formation structure more finely. Using the standard seismic wave reflection signals and drilling data, a three-dimensional model of the oilfield subsurface formation is constructed, making the analysis of geological structure more intuitive and providing accurate formation structure maps for decision-making. Through four-dimensional dynamic monitoring in step S14, the subsurface structure changes can be updated in real time, providing support for risk estimation and optimization in oilfield exploitation, improving the safety and effectiveness of exploitation. Through high-precision dynamic monitoring data, engineers can assess the exploitation status in a timely manner and adjust the exploitation strategy, thereby improving the efficiency and resource utilization rate of oilfield exploitation.

[0016] Preferably, step S13 includes the following steps:

[0017] Perform well logging speed analysis on the oilfield drilling data to generate oilfield logging speed data; perform seismic velocity inversion on the standard seismic wave reflection signals according to the oilfield logging speed data to generate oilfield regional seismic wave velocity data; 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 oilfield regional seismic wave velocity data to generate geological layer depth information data;

[0018] 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 subsurface layer geological interface data;

[0019] Stratum attribute information data is extracted from the underground hierarchical geological interface data, and a three-dimensional stratum structure framework data is generated based on the reflection interface modeling based on the underground hierarchical geological interface data; and the three-dimensional stratum structure framework data is subjected to initial stratum attribute assignment through the stratum attribute information data, and three-dimensional modeling data of the underground stratum of the oilfield is generated.

[0020] The well logging velocity data is generated through well logging velocity analysis, ensuring accurate inversion of the seismic wave reflection signal, improving the accuracy of the seismic wave velocity data, and thus better reflecting the actual situation of the underground stratum. The depth linear conversion is performed on the reflection wave trend point data to obtain geological hierarchical depth information, effectively eliminating the depth error caused by the difference in seismic velocity, and providing accurate stratum depth data for subsequent modeling. The Fourier transform is used to generate seismic wave reflection spectrum data, and the reflection interface is identified in combination with the depth information, which can effectively distinguish different stratum interfaces, improve the resolution of the hierarchical interface, and help identify complex geological structures. The extracted stratum attribute information data not only covers the geological features, but also further provides accurate data basis for attribute assignment, which is helpful for the detailed description of the stratum attribute and improves the accuracy of modeling. The three-dimensional stratum structure framework model is constructed based on the stratum interface data, and a complete stratum three-dimensional model is generated through attribute assignment, so that the underground stratum structure of the oilfield is more intuitive and detailed, and a clear structure model is provided for the geological engineering personnel. The generated three-dimensional model of the underground stratum of the oilfield can dynamically reflect the attributes and structure of the stratum, facilitate real-time optimization of decisions in oilfield exploration, development and management, and improve the development efficiency and safety of oilfield resources. Step S13 not only enhances the identification accuracy of the geological hierarchy, but also constructs a high-precision, information-rich three-dimensional stratum model through multi-level data fusion and inversion processing, which provides important support for the optimized development and effective management of oilfield resources.

[0021] Preferably, step S14 comprises the following steps:

[0022] Step S141: confirming a time reference point of the three-dimensional modeling data of the underground stratum of the oilfield to obtain monitoring time node data; and collecting repeated seismic exploration data based on the monitoring node time data to obtain a seismic exploration time series data set;

[0023] Step S142: performing seismic data difference analysis on the seismic exploration time series data set to generate a time difference stratum change profile; and performing stratum attribute comparison analysis on the time difference stratum change profile to generate a stratum attribute change distribution map;

[0024] Step S143: updating the dynamic geological data of the three-dimensional modeling data of the underground formation of the oilfield by the formation attribute change map to generate the dynamic monitoring data of the structure of the underground formation of the oilfield, wherein the dynamic geological data updating includes dynamic updating of the structural morphology and dynamic updating of the attribute.

[0025] The present application can ensure consistent seismic data acquisition of the oilfield formation under the same time sequence through the time reference point confirmation and monitoring time node setting, provide a reliable time reference for formation change monitoring, and enhance the comparability of the monitoring data. Through the seismic time sequence data difference analysis of step S142, the time difference formation change profile is generated to help identify the change of the formation between different time nodes, and then the formation attribute change distribution map is generated. Such difference analysis helps to quickly identify the small geological changes occurring in the oilfield and effectively warn potential problems. In step S143, the dynamic geological data is updated using the formation attribute change map, including real-time updating of the structural morphology and attribute, so that the three-dimensional model of the underground formation of the oilfield can reflect the latest state of the formation. This updating method ensures the dynamic adaptability of the model and improves the scientificity of the decision-making in the oilfield development process. The dynamic monitoring data can help geologists assess the impact of oilfield development on the formation structure in a timely manner, providing data support for optimizing the mining plan, reducing resource waste, and reducing environmental impact. The dynamically updated formation data can provide safety protection during the mining process and help identify potential geological risks to provide real-time support for the safety of oilfield operations.

[0026] Preferably, step S2 comprises the following steps:

[0027] Step S21: analyzing the underground oil and gas distribution according to the dynamic monitoring data of the structure of the underground formation of the oilfield to generate an underground oil and gas distribution map; calculating the total amount of oil and gas storage based on the underground oil and gas distribution map to obtain total oil and gas storage data; and calculating the oil and gas mining amount based on the total oil and gas storage data to obtain oil and gas mining amount data;

[0028] Step S22: performing data difference calculation on the total oil and gas storage data and the oil and gas mining amount data to generate remaining oil and gas mining data; comparing the remaining oil and gas mining data with a preset residual oil and gas reserve threshold value, and when the remaining oil and gas mining data is less than or equal to the residual oil and gas reserve threshold value, performing regional gridding on the underground oil and gas distribution map based on the remaining oil and gas mining data to generate an underground oil and gas residual regional grid.

[0029] Step S23: performing regional geological hardness analysis on the underground oil and gas residual regional grid based on the dynamic monitoring data of the structure of the underground formation of the oilfield to generate grid regional geological hardness data; and performing remaining oil flow path analysis on the underground oil and gas residual regional grid based on the grid regional geological hardness data to generate remaining oil grid flow path data.

[0030] The present application provides accurate resource distribution map and reserve data for oilfield exploitation through underground oil and gas distribution analysis and oil and gas storage calculation, which is convenient for accurately mastering the distribution and exploitation of oil and gas resources, and realizes more scientific resource management. Through the remaining oil and gas data calculation and threshold comparison of step S22, the remaining reserves of the oilfield are monitored in real time, so that the exploitation strategy can be adjusted in time when the reserves approach the preset threshold, resource waste is avoided, and the exploitation efficiency is improved. When the remaining oil and gas reserves are lower than the threshold, the system will divide the oil and gas distribution into regional grids, so that the management of low reserve areas is more refined, which helps to concentrate on the exploitation of high-efficiency residual resource areas, and improves the resource utilization rate. The geological hardness analysis of step S23 provides geological difficulty evaluation of the grid area, which provides a scientific basis for drilling and exploitation difficulty in the exploitation process, helps to avoid risks and reduce exploitation difficulty and cost. The flow path of residual oil is analyzed by using the geological hardness data of the grid area, which helps to plan the optimal exploitation path, facilitates efficient extraction of residual oil resources, and improves the ultimate recovery rate of oil and gas resources. Based on the dynamic monitoring and regional analysis of the fine management, the exploitation process is more safe and controllable, the resource waste is reduced, the exploitation strategy is optimized, and the efficiency and economy of the oilfield operation are enhanced.

[0031] Preferably, the remaining oil and gas exploitation data and the preset residual oil and gas reserve threshold are compared, and when the remaining oil and gas exploitation data is less than or equal to the residual oil and gas reserve threshold, the underground oil and gas distribution map is divided into regional grids based on the remaining oil and gas exploitation data, including:

[0032] The remaining oil and gas exploitation data and the preset residual oil and gas reserve threshold are compared, and when the remaining oil and gas exploitation data is less than or equal to the residual oil and gas reserve threshold, the underground oil and gas distribution map is divided into initial grid cells based on the remaining oil and gas exploitation data, to generate initial grid cell data, wherein the initial grid cell data includes formation properties and oil and gas properties;

[0033] The formation properties and oil and gas properties are calculated to obtain formation-oil and gas grid ratio data; the initial grid cell data is balanced according to the formation-oil and gas grid ratio data to generate internal adjustment data of the grid cell; and the oil and gas dynamic displacement analysis is performed on the oil and gas properties to generate oil and gas dynamic displacement data;

[0034] The initial grid cell data is adaptively adjusted according to the oil and gas dynamic displacement data to generate external adjustment data of the grid cell; and the initial grid cell data is assigned with grid numbers based on the internal adjustment data of the grid cell and the external adjustment data of the grid cell to generate the underground oil and gas residual area grid.

[0035] The application can automatically identify low reserve areas and conduct fine grid management by comparing the remaining oil and gas exploitation data with the threshold value and initial grid cell division, which is convenient for accurate positioning and exploitation of remaining resources, and realizes more fine oil and gas distribution control. The calculation of the proportion of strata and oil and gas properties in the step and the balance adjustment ensure that the proportion of strata and oil and gas distribution in each grid cell is rationalized, which helps to accurately evaluate the oil and gas reserves in the region, avoids the deviation of resource estimation, and improves the scientificity of oilfield remaining resource management. Through dynamic displacement analysis of oil and gas properties, oil and gas dynamic displacement data are generated to help predict the flow and diffusion path of oil and gas in the grid cell, so that the exploitation operation can be more accurate and the waste of resources caused by uncertain flow can be reduced. Based on the grid boundary adaptive adjustment of the oil and gas dynamic displacement data, the grid boundary can be updated in real time with the dynamic change of oil and gas, so that the boundary accuracy of each grid cell is higher, and the resource recovery rate is further optimized. The grid cell data after internal and external adjustment is numbered and distributed to form a unique identification of the underground oil and gas residual area grid, which is convenient for subsequent management and data retrieval, and improves the traceability and visualization of oilfield exploitation information. Dynamic adjustment of the grid management and detailed flow path analysis significantly improve the recovery rate of remaining oil and gas resources, while optimizing the exploitation path and strategy, reducing the cost and environmental impact of oilfield exploitation, and improving the economic efficiency and sustainability of oilfield operation. The grid management and dynamic optimization of oilfield exploitation are effectively supported, realizing efficient utilization and scientific management of residual oil and gas resources, and helping efficient exploitation and accurate management of oilfield resources.

[0036] Preferably, step S23 comprises the following steps:

[0037] 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 oilfield, and generate grid area geological hardness data; wherein the formula of the geological hardness calculation is as follows:

[0038]

[0039] In the formula, is the geological hardness, is the elastic modulus, is the rock density;

[0040] Step S232: According to the grid area geological hardness data, the low hardness grid of the underground oil and gas residual area grid is screened to obtain the low hardness screening grid; the low hardness screening grid is analyzed for geological porosity to generate low hardness grid geological porosity data;

[0041] Step S233: Simulate the remaining oil and gas flow path 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;

[0042] Step S234: Path visualization is performed on the oil and gas flow path simulation data, thereby generating remaining oil grid flow path data.

[0043] The present application calculates the geological hardness by a formula The geological hardness is quantified, the rock hardness of the grid area is parameterized, low hardness areas are identified, the basis for dynamic layer management is provided, and the adaptability to different geological areas in the oil and gas production process is improved. By screening the low hardness grid, the system can focus on the low hardness area with high porosity, realize directional exploitation of resources, reduce unnecessary drilling and exploration work, and reduce the difficulty and cost of oil and gas production. After the geological porosity analysis of the low hardness grid, the flow characteristics of oil and gas in the residual area are further clarified. The porosity data helps to identify the movement ability of resources in the underground grid, 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 the low hardness grid geological porosity data, oil and gas flow path simulation data is generated, which enables the production personnel to predict and optimize the flow path of oil and gas, reduce resource waste, and maximize the production effect. Through the visualization of the flow path, the generated remaining oil grid flow path data can intuitively show the flow trend and direction of oil and gas, providing comprehensive visual data support for the production of oil fields, and facilitating the formulation of accurate production plans in complex geological structures. Through low hardness screening and path optimization, overproduction of high hardness and low porosity areas is avoided, which not only saves production resources but also reduces environmental impact, improves the environmental friendliness and sustainability of oil fields.

[0044] Preferably, step S3 comprises the following steps:

[0045] Step S31: Marking flow boundary points of the underground oil and gas residual area grid according to the remaining oil grid flow path data to obtain regional grid oil and gas flow boundary points; and using the regional grid oil and gas flow boundary points to splice adjacent grids of the underground oil and gas residual area grid to generate a flow spliced grid of the underground oil and gas residual area;

[0046] Step S32: Connecting the remaining oil grid flow path data through the flow spliced grid of the underground oil and gas residual area to generate a remaining oil flow path network; and calculating the oil volume aggregation of the remaining oil flow path network according to the gravitational acceleration to generate an oil volume flow aggregation, wherein the formula for the oil volume aggregation calculation is as follows:

[0047]

[0048] In the formula, represents the flow oil volume, represents the oil density, represents the gravitational acceleration, Represented as the flow cross-sectional area, Represented as flow height, Expressed as the viscosity of the oil;

[0049] Step S33: Return the oil flow accumulation amount to step S22 and compare it with the preset residual oil and gas reserve threshold again. When the oil flow accumulation amount is greater than or equal to the preset residual oil and gas reserve threshold, mark the high-density area of ​​the underground oil and gas residual area flow splicing grid based on the oil flow accumulation amount, thereby generating a high-density area of ​​oil flow.

[0050] This invention, by marking flow boundary points, enables the system to accurately identify the boundary regions of oil and gas flows, providing a foundation for refined management of residual oil and gas flows, preventing resource leakage, and ensuring the stability of flow paths. The splicing of adjacent grids ensures the continuity of residual oil and gas flow between regional grids, thereby generating a spliced ​​flow grid. This splicing method effectively connects the flow paths of oil and gas in different regions, avoiding flow restriction problems caused by path interruptions. The oil accumulation calculation formula... The flow accumulation of remaining oil is calculated. This formula considers multiple parameters such as flow cross-sectional area, flow height, oil density, and viscosity, making the accumulation calculation results more accurate and providing data support for efficient extraction. Based on the comparison between the flow accumulation and the residual oil and gas reserve threshold, high-accumulation areas are marked, ensuring the system prioritizes oil-rich grid areas. This method not only increases oil and gas production but also significantly improves resource utilization efficiency. Backtracking of accumulation and re-comparison with the threshold ensures the system can dynamically adjust based on actual data, achieving continuous monitoring and optimization of the oil and gas extraction process. Simultaneously, this cyclical comparison mechanism provides flexible strategy adjustments for different extraction stages, ensuring the effectiveness of the extraction plan. Step S3 refines resource accumulation and path planning, making extraction activities more targeted and cost-effective. By identifying and accumulating high-concentration oil and gas areas, resource waste from inefficient extraction is significantly reduced, improving the economics of extraction.

[0051] Preferably, step S4 includes the following steps:

[0052] Step S41: Perform regional residual oil monitoring in areas with high oil flow concentration to generate residual oil monitoring data for high concentration areas;

[0053] Step S42: Based on the residual oil monitoring data of high-aggregation areas, the effective areas of high-aggregation areas of oil flow are screened to obtain the effective area data of high-aggregation residual oil.

[0054] Step S43: residual oil distribution rule analysis is performed on the residual oil high aggregation effective area data to generate residual oil distribution rule analysis data; and residual oil exploitation strategies are constructed based on the residual oil distribution rule analysis data and oilfield drilling data, thereby generating residual oil exploitation strategies.

[0055] The high aggregation effective area residual oil monitoring data generated by the regional residual oil monitoring can identify effective areas with high residual oil aggregation, effectively reducing the exploitation of inefficient areas and providing data support for accurate oil and gas exploitation. The high aggregation effective area residual oil monitoring data is obtained by further screening the high aggregation effective area, which ensures that resources are concentrated in areas with abundant residual oil, reduces unnecessary resource waste, and improves the economic benefits of exploitation. Through residual oil distribution rule analysis of the effective area, the system can predict and master the spatial distribution characteristics of residual oil, and the generated residual oil distribution rule analysis data provides a scientific basis for subsequent exploitation, ensuring that the exploitation strategy is based on actual data and improving the accuracy of the exploitation strategy. The residual oil distribution rule analysis data is used to construct residual oil exploitation strategies in combination with oilfield drilling data, making the exploitation strategies more in line with the actual situation of the oilfield. This strategy fundamentally improves the efficiency and yield of residual oil recovery. By constructing customized residual oil exploitation strategies, the development of non-effective areas is reduced. This development of high aggregation effective areas not only improves the output of residual oil, but also reduces the cost of ineffective drilling and exploration. The regional residual oil monitoring and residual oil exploitation strategy construction 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 remain efficient and scientific at each stage.

[0056] Preferably, the residual oil distribution rule analysis of the residual oil high aggregation effective area data includes:

[0057] The residual oil high aggregation effective area data is subjected to regional residual oil characteristic extraction to obtain high aggregation effective area residual oil characteristic data; the high aggregation effective area residual oil characteristic data is subjected to data set division to generate a model training set and a model test set; a model training set is subjected to model training through a decision tree algorithm to generate a residual oil distribution rule prediction pre-model;

[0058] The residual oil distribution rule prediction pre-model is subjected to model optimization iteration according to the model test set, thereby generating a residual oil distribution rule prediction model; the residual oil high aggregation effective area data is imported into the residual oil distribution rule prediction model for residual oil distribution rule analysis, thereby generating residual oil distribution rule analysis data.

[0059] The present application extracts the characteristics of residual oil in the high-concentration effective area, ensuring the accuracy and representativeness of the model input data, thereby providing high-quality data basis for residual oil distribution rule analysis. The residual oil characteristic data is divided into training set and test set to ensure the training and test effect of the model, so that the model has higher generalization ability, thereby improving the prediction accuracy. The decision tree algorithm is used to train the training set to generate a residual oil distribution rule prediction pre-model. The decision tree algorithm has good interpretability and high efficiency, so that the model is more efficient in predicting the residual oil distribution rule. The test set is used to optimize and iterate the prediction pre-model, so that the model continuously adjusts and optimizes the parameters, and finally generates a high-precision residual oil distribution rule prediction model, laying a solid foundation for subsequent data analysis. After importing the high-concentration effective area data into the prediction model, residual oil distribution rule analysis data is generated, revealing the spatial distribution trend and characteristics of residual oil. This data provides strong support for formulating accurate exploitation strategies and ensures that exploitation activities have a basis. By accurately mastering the residual oil distribution rule, the exploitation scheme can be implemented in the high-concentration oil area, significantly improving the development efficiency of the oilfield, while reducing the development investment in the low-efficiency area, further improving the resource utilization rate and economic return. The prediction model helps to identify and preferentially exploit the high-concentration area of residual oil, avoiding blind exploitation, so that the oilfield resources can be more reasonably managed and utilized.

[0060] The beneficial effects of the present application are that by obtaining oilfield exploitation location information data and oilfield drilling data, the basic integrity of the data is ensured, providing accurate geographical and technical background for subsequent analysis. By collecting standard seismic wave reflection signals, the stratigraphic structure of the oilfield can be clearly identified, promoting more accurate underground imaging. Real-time dynamic monitoring of the geological structure changes of the oilfield helps to timely adjust the exploitation strategy and optimize resource allocation. The underground oil and gas distribution map generated based on dynamic monitoring data can provide spatial distribution information of oil and gas, helping to identify oil and gas enrichment areas. Calculating the exploitation data of remaining oil and gas helps to evaluate the development potential and economic benefits of the oilfield. The underground oil and gas distribution map is regionally gridded, which can refine the analysis and make the subsequent flow path analysis more accurate. Clearly defining the flow path of remaining oil helps to optimize the exploitation strategy and improve the recovery rate of remaining oil. By splicing adjacent grids, the data of different regions can be integrated to form a more comprehensive residual oil and gas region flow splicing grid. Accurate calculation of oil flow aggregation provides a basis for subsequent high aggregation area identification. Marking high aggregation areas helps to quickly locate the most valuable development areas and improve exploitation efficiency. By screening oil flow high aggregation areas, the efficiency of development resources is ensured, and invalid exploitation is avoided. Analysis of high aggregation effective areas can reveal the distribution characteristics of remaining oil and optimize subsequent exploitation strategies. The residual oil exploitation strategy based on analysis data makes the exploitation process more scientific and systematic. Therefore, the present application improves the accuracy and practicality of remaining oil distribution rule analysis through dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification, and systematic exploitation strategy construction. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 A step flow diagram of a residual oil distribution rule analysis method;

[0062] Figure 2 A detailed implementation step flow diagram of step S2 in Figure 1

[0063] A detailed implementation step flow diagram of step S3 in Figure 3 Figure 1 A detailed implementation step flow diagram of step S4 in

[0064] Figure 4 Figure 1 A detailed implementation step flow diagram of step S4 in

[0065] The implementation of the present application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0066] ​​The technical method of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0067] In addition, the drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the drawings represent identical or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0068] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the example embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0069] To achieve the above-mentioned purpose, please refer to Figures 1 to 4 A remaining oil distribution rule analysis method, the method comprising the following steps:

[0070] Step S1: Obtain oilfield exploitation position information data and oilfield drilling data; collect seismic wave reflection signals according to the oilfield exploitation position 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;

[0071] 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; perform exploitation remaining oil and gas difference calculation on the underground oil and gas distribution map to generate remaining oil and gas exploitation data; perform regional gridding on the underground oil and gas distribution map based on the remaining oil and gas exploitation data to generate an underground oil and gas residual regional grid; perform remaining oil flow path analysis on the underground oil and gas residual regional grid to generate remaining oil grid flow path data;

[0072] Step S3: adjacent grid splicing is performed on the underground oil and gas residual area grid according to the residual oil grid flow path data, to generate an underground oil and gas residual area flow splicing grid; oil flow aggregation calculation is performed on the underground oil and gas residual area flow splicing grid, to generate an oil flow aggregation amount; and the underground oil and gas residual area flow splicing grid is marked with a high aggregation degree area through the oil flow aggregation amount, to generate an oil flow high aggregation degree area.

[0073] Step S4: effective area screening is performed on the oil flow high aggregation degree area, to obtain residual oil high aggregation degree effective area data; residual oil distribution rule analysis data is generated through residual oil distribution rule analysis on the residual oil high aggregation degree effective area data; and residual oil exploitation strategy construction is performed on the oilfield drilling data based on the residual oil distribution rule analysis data, to generate a residual oil exploitation strategy.

[0074] The present application ensures the basic accuracy of the analysis and monitoring data by obtaining oilfield exploitation location information and drilling data. This process lays a solid data foundation for subsequent seismic wave reflection signal acquisition and dynamic monitoring. Four-dimensional geological structure dynamic monitoring using standard seismic wave reflection signals can capture 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. Oil and gas distribution analysis based on dynamic monitoring data of underground stratum structure helps to generate more accurate underground oil and gas distribution maps, thereby providing effective visual support and data basis for subsequent exploitation decisions. Through exploitation residual oil and gas difference calculation on the underground oil and gas distribution map, the exploitation effect and resource residual condition can be evaluated in a timely manner, providing data support for adjusting the exploitation strategy. Regional gridding of the oil and gas distribution map generates underground oil and gas residual area grids, which helps fine-grained management and regional division, enhancing the spatial management capability of underground resources. Residual oil flow path analysis can reveal the dynamic distribution and flow trend of oil and gas underground, providing important clues for formulating scientific exploitation strategies and reducing resource waste. Calculation of oil flow aggregation and marking of high aggregation degree areas can effectively identify priority exploitation areas, improving exploitation efficiency and economic benefits and ensuring the sustainable profitability of the oilfield. Effective area screening of the high aggregation degree area ensures that exploitation activities are concentrated in areas with economic benefits, thereby avoiding unnecessary resource waste and environmental damage. The exploitation strategy formulated based on residual oil distribution rule analysis data makes oilfield development more scientific and reasonable, improving overall exploitation efficiency. Therefore, the present application improves the precision and practicality of residual oil distribution rule analysis through dynamic monitoring, fine-grained oil and gas distribution analysis, efficient flow path identification, and systematic exploitation strategy construction.

[0075] In the embodiment of the present application, reference is made to Figure 1The application discloses a remaining oil distribution rule analysis method and a remaining oil distribution rule analysis device.

[0076] Step S1: acquiring oilfield exploitation position information data and oilfield drilling data; collecting standard seismic wave reflection signals by performing seismic wave reflection signal collection according to the oilfield exploitation position information data; and generating oilfield underground formation structure dynamic monitoring data by performing four-dimensional geological structure dynamic monitoring on the standard seismic wave reflection signals by using the oilfield drilling data;

[0077] In the embodiment of the application, the oilfield exploitation position information data is acquired from an oilfield management system or a geological survey agency, and includes information such as coordinates, exploitation depth, and oil and gas types of each well site. Related oilfield drilling data is collected, including drilling logs, formation descriptions, rock physical properties, and historical exploitation data. The data formats are ensured to be consistent (such as CSV, Excel, or a database) to facilitate subsequent data processing and analysis. A seismic exploration scheme is formulated according to the exploitation position information data, including selecting appropriate exploration methods (such as reflection seismic, source type, and receiver arrangement). Collection parameters such as sampling frequency and source excitation mode are determined to ensure signal quality. Field exploration is performed, seismic waves are excited by using a source (such as explosion, weight or vibrator), and reflection signals are collected by using a seismic receiver (such as a seismograph or a sensor). The collected signal data includes time series seismic wave reflection signals. The collected seismic wave reflection signals are preliminarily processed, such as denoising, gain adjustment, and time correction, to obtain standardized seismic wave reflection signals. The standardized seismic wave reflection signals are combined with the oilfield drilling data, and four-dimensional geological structure dynamic monitoring technology is adopted. This process includes analysis of multiple dimensions such as time, space, depth, and geological characteristics. A dynamic monitoring model is established by combining the seismic wave reflection signals and the drilling data. The changes of the underground structure are analyzed by using the time difference and amplitude change of the reflection wave, time series seismic wave reflection data are generated, and the historical data are compared to identify the dynamic changes of the formation. According to the analysis results, the oilfield underground formation structure dynamic monitoring data are generated, including information such as formation depth, structure change, and fluid movement. These data can provide an important basis for subsequent oil and gas exploration and exploitation.

[0078] Step S2: performing 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; performing remaining oil and gas difference calculation on the underground oil and gas distribution map to generate remaining oil and gas exploitation data; performing regional gridding on the underground oil and gas distribution map based on the remaining oil and gas exploitation data to generate an underground oil and gas residual regional grid; and performing remaining oil flow path analysis on the underground oil and gas residual regional grid to generate remaining oil grid flow path data.

[0079] In the embodiments of the present application, by collecting and organizing oilfield underground formation structure dynamic monitoring data, the data integrity and accuracy are ensured, including formation thickness, lithology, porosity, saturation and other parameters. Using formation monitoring data, an underground oil and gas distribution model is built by three-dimensional modeling software (such as Petrel or GeoGraphix). The oil and gas distribution is predicted by using geostatistical methods (such as Kriging interpolation method), 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 reservoir, 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 reserves in the oilfield are calculated. The formula is: R = S - E; where R is the remaining oil and gas reserves, S is the total oil and gas storage, and E is the produced oil and gas. After the calculation is completed, the remaining oil and gas data is organized into a database for subsequent analysis and decision-making. The remaining oil and gas production data is combined with the underground oil and gas distribution map for regional gridding processing. According to the set grid size (such as 100m x 100m), the underground oil and gas distribution map is divided into multiple grid cells. In each grid cell, the oil and gas storage, production and remaining amount are recorded, and underground oil and gas residual regional grid data is generated. Based on the underground oil and gas residual regional grid data, a remaining oil flow path analysis model is established using fluid dynamics principles. Factors such as permeability, porosity, oil and gas flow resistance are considered. 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 in the ground. The flow path data is recorded to identify the main oil and gas flow channels and retention areas. The simulation results are summarized to form remaining oil grid flow path data, and the oil and gas flow path map is visualized to facilitate subsequent analysis and decision-making.

[0080] Step S3: According to the remaining oil grid flow path data, the adjacent grid of the underground oil and gas residual regional grid is spliced to generate the underground oil and gas residual regional flow splicing grid; the oil amount aggregation calculation is performed on the underground oil and gas residual regional flow splicing grid to generate the oil flow aggregation amount; the underground oil and gas residual regional flow splicing grid is marked with high aggregation degree area through the oil flow aggregation amount, thereby generating the oil flow high aggregation degree area;

[0081] In the embodiment of the present application, adjacent grid cells are identified based on the remaining oil grid flow path data. These grid cells have flow path connectivity and are suitable for splicing. The adjacent grids are spliced using a grid splicing algorithm (such as the Delaunay triangulation method or the Voronoi diagram method). The continuity of the flow path is ensured during the splicing process. The spliced grid is marked as "underground oil and gas residual area flow splicing grid". After splicing, a new grid data structure is generated, which contains the comprehensive information of the spliced area, including oil and gas properties, flow path, etc. The oil flow accumulation is calculated using the following formula: In the formula, represents the flow oil amount, represents the oil density, represents the gravitational acceleration, represents the flow cross-sectional area, represents the flow height, represents the oil viscosity; for each flow splicing grid, the oil accumulation is calculated using oil and gas property data (such as density, flow cross-section, and height). The accumulation data of all flow splicing grids is summarized to obtain the overall oil flow accumulation. The calculation results are arranged into a dataset for subsequent analysis and decision-making. A threshold value of oil flow accumulation is preset, and a reasonable threshold value is set according to historical data and expert experience to identify high accumulation areas. Compare the oil flow accumulation with the preset accumulation threshold value: when the oil flow accumulation is greater than or equal to the accumulation threshold value, mark the splicing grid as "high accumulation area". Record the grid information of the marked high accumulation area to form a new dataset. Finally, the "oil flow high accumulation area" data is generated for subsequent development, management, and monitoring.

[0082] Step S4: Effective area screening is performed on the oil flow high accumulation area to obtain residual oil high accumulation effective area data; residual oil distribution rule analysis is performed on the residual oil high accumulation effective area data to generate residual oil distribution rule analysis data; and residual oil exploitation strategy is constructed based on the residual oil distribution rule analysis data and oilfield drilling data, thereby generating the residual oil exploitation strategy.

[0083] In the embodiment of the present application, by referring to the data of the high-aggregated area of oil flow, the high-aggregated area of residual oil is first screened out. Each area is evaluated to ensure that it meets certain effectiveness standards, such as the connectivity of the oil and gas flow path and whether the aggregated oil quantity meets the minimum requirement for economic exploitation. The area meeting the effectiveness standards is marked as a "high-aggregated effective area of residual oil", and the relevant attribute data is recorded for subsequent analysis and use. The geological features, oil and gas characteristics and other related data of the effective area are integrated to form a "high-aggregated effective area of residual oil data" set. The high-aggregated effective area of residual oil data is subjected to regional residual oil characteristic extraction to obtain a data set containing residual oil characteristics (such as distribution pattern, concentration, etc.). The high-aggregated effective area of residual oil characteristic data is subjected to data set division to generate a model training set and a model test set for subsequent model construction and verification. A decision tree algorithm or other machine learning method is used to train the model training set to generate a residual oil distribution rule prediction pre-model. The residual oil distribution rule prediction pre-model is optimized and iterated according to the model test set to form a more accurate residual oil distribution rule prediction model. The high-aggregated effective area of residual oil data is input into the residual oil distribution rule prediction model for analysis to generate "residual oil distribution rule analysis data" to provide a deep understanding of the distribution and dynamic characteristics of residual oil and gas. Based on the generated "residual oil distribution rule analysis data", the exploitation potential and economic benefits of each area are analyzed to provide a basis for the development of residual oil exploitation strategies. In combination with the oilfield drilling data, the current exploitation technology and equipment are evaluated, and an optimized residual oil exploitation strategy is developed according to the residual oil distribution rule. The optimized residual oil exploitation strategy mainly includes the selection and improvement of exploitation technology, the optimized layout of drilling locations, and the exploitation time arrangement and resource allocation strategy. The developed exploitation strategy is simulated and evaluated to ensure that it is feasible in actual operation, and appropriate adjustments are made according to feedback.

[0084] Preferably, step S1 comprises the following steps:

[0085] Step S11: obtaining oilfield exploitation location information data and oilfield drilling data;

[0086] Step S12: transmitting seismic waves according to the oilfield exploitation 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;

[0087] Step S13: performing three-dimensional oilfield subsurface formation modeling on the standard seismic wave reflection signals using the oilfield drilling data to generate three-dimensional oilfield subsurface formation modeling data;

[0088] Step S14: performing four-dimensional geological structure dynamic monitoring on the three-dimensional oilfield subsurface formation modeling data to generate oilfield subsurface formation structure dynamic monitoring data.

[0089] In the embodiments of the present application, the exploitation location information of the oilfield is obtained by using geographic information system (GIS) and remote sensing technology, including geographic coordinates, topographic features, etc. The historical drilling records are obtained from the relevant database or oilfield management system, including drilling depth, rock type, porosity, permeability, and other important geological information. The above data is integrated into a unified database for subsequent analysis and modeling. According to the oilfield exploitation location information, 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 levels are covered. High-sensitivity receivers are used to record the seismic wave reflection signals synchronously, capturing the signals reflected from the underground levels. Signal processing techniques (such as wavelet transform) are applied to remove background noise. The signals are standardized to have a uniform amplitude range for subsequent analysis. The long-term trend in the signals is removed to highlight short-term variation characteristics. The preprocessed standard seismic wave reflection signals are analyzed using oilfield drilling data to extract characteristic parameters. Inversion algorithms (such as wave inversion) are used to convert seismic wave data into underground structure information. Combined with known drilling data, a three-dimensional model of the oilfield underground strata is established to show the rock properties and distribution of different levels. The three-dimensional model data, including stratum thickness, physical properties (such as density, velocity, etc.), and spatial position, are output. Key geological parameters to be monitored, such as stratum deformation and fluid flow, are determined. The three-dimensional modeling data are combined with the time dimension to establish a dynamic monitoring model. Time series data are used to monitor stratum changes in real time, and reflection signals at different time nodes are collected. Data mining and analysis tools are used to extract 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.

[0090] Preferably, step S13 comprises the following steps:

[0091] The well logging velocity analysis is performed on the oilfield drilling data to generate oilfield well logging velocity data; the seismic velocity inversion is performed on the standard seismic wave reflection signal according to the oilfield well logging velocity data to generate oilfield area seismic wave velocity data; the reflection wave travel time picking is performed on the standard seismic wave reflection signal to generate seismic reflection wave trend point data; the depth linear conversion is performed on the seismic reflection wave trend point data and the oilfield area seismic wave velocity data to generate geological layer level depth information data;

[0092] The Fourier transform is performed on the standard seismic wave reflection signal to generate seismic wave reflection spectrum data; the reflection interface identification is performed on the seismic wave reflection spectrum data according to the geological layer level depth information data to obtain underground layer level geological interface data;

[0093] Stratum attribute information data is obtained by extracting stratum attribute information from the underground hierarchical geological interface data; a stratum modeling based on a reflection interface is performed based on the underground hierarchical geological interface data, and three-dimensional stratum structure framework data is generated; and initial stratum attribute assignment is performed on the three-dimensional stratum structure framework data by using the stratum attribute information data, and oilfield underground stratum three-dimensional modeling data is generated.

[0094] In the embodiment of the present application, by collecting oilfield drilling data, including drilling records, porosity, permeability and other information, the logging tool type and logging method (such as resistivity, acoustic, density logging, etc.) are determined. The logging data is analyzed by using logging data analysis software (such as Geographix, Petrel, etc.), and the acoustic velocity at different depth levels is calculated (for example, using acoustic logging data), and the oilfield logging velocity data is generated, and the acoustic velocity value and its depth of each stratum are recorded. The velocity inversion algorithm suitable for the oilfield (such as the least square inversion method, the inversion method based on genetic algorithm) is selected. The standard seismic wave reflection signal is inverted using the oilfield logging velocity data, and the seismic wave velocity of the oilfield area is calculated, and the oilfield area seismic wave velocity data is generated, and the seismic wave propagation velocity information of each stratum is provided. The standard seismic wave reflection signal is analyzed, and the start and end times of each reflection wave are identified. The travel time information of each reflection wave is recorded, and the seismic reflection wave trend point data is generated, including the time domain position and depth information of the reflection wave. The reflection wave trend point data and the oilfield area seismic wave velocity data are used to convert the travel time data into depth information by using the depth linear conversion algorithm, and the geological hierarchical depth information data is generated, and the depth and geological distribution of each stratum are provided. The standard seismic wave reflection signal is subjected to Fourier transform to convert it into a frequency domain signal to extract frequency components, and the seismic wave reflection spectrum data is generated to record the reflection wave energy distribution at different frequencies. Based on the geological hierarchical depth information data and the seismic wave reflection spectrum data, the reflection interface is identified by using the adaptive threshold method or the machine learning algorithm, and the underground hierarchical geological interface data is obtained, and the reflection interface position and attribute information of each stratum are recorded. The underground hierarchical geological interface data is analyzed, and the attribute information of each stratum, such as density, porosity, etc., is extracted, and the stratum attribute information data is obtained, and the physical property record of each stratum is formed. Based on the underground hierarchical geological interface data, a stratum model is established by using the reflection interface method, the geometry and physical properties of each stratum are defined, and the three-dimensional stratum structure framework data is generated to describe the three-dimensional stratum structure of the entire oilfield. According to the stratum attribute information data, the three-dimensional stratum structure framework data is initially assigned to each stratum to ensure that each stratum has physical properties, and the oilfield underground stratum three-dimensional modeling data is generated to form a complete underground stratum model for subsequent analysis and development.

[0095] Preferably, step S14 comprises the following steps:

[0096] Step S141: Time reference point confirmation is performed on the three-dimensional modeling data of the oilfield underground formation to obtain monitoring time node data; repeated seismic exploration data collection is performed on the three-dimensional modeling data of the oilfield underground formation based on the monitoring node time data to obtain a seismic exploration time series data set;

[0097] Step S142: Seismic data difference analysis is performed on the seismic exploration time series data set to generate a time difference formation change profile; formation attribute comparison analysis is performed on the time difference formation change profile to generate a formation attribute change distribution map;

[0098] Step S143: Dynamic geological data updating is performed on the three-dimensional modeling data of the oilfield underground formation through the formation attribute change map to generate oilfield underground formation structure dynamic monitoring data, wherein the dynamic geological data updating includes dynamic updating of the structural morphology and dynamic updating of the attribute.

[0099] In the embodiment of the present application, the time nodes for monitoring are determined, such as the initial time of seismic exploration, the time interval of each exploration, and the time point of subsequent updating. The monitoring time node data is generated by using a project management tool and a time series analysis method, and the exact time of each node is recorded. Based on the monitoring node time data, a repeated seismic exploration scheme is designed, including the parameters and equipment configuration of the emission source and the recording of the received signal. The repeated seismic exploration operation is performed, and the seismic exploration time series data set is collected, including the seismic wave reflection signals at different time nodes. The seismic exploration time series data sets at different time nodes are compared and analyzed to identify the changes in the seismic wave reflection signals. The time difference formation change is calculated, and the signal difference caused by the change in the underground formation is extracted. The time difference formation change profile is drawn by using a seismic data processing software (such as Seismic Unix, Petrel, etc.), and the formation change between different time nodes is directly displayed. The obvious change area is marked on the profile to help subsequent analysis. The formation attributes in the time difference formation change profile are compared and analyzed to determine the physical property changes (such as density, porosity, etc.) of each formation, and the formation attribute change distribution map is generated to record the attribute change of each layer and provide visual data support. Based on the formation attribute change distribution map, the three-dimensional modeling data of the oilfield underground formation is dynamically updated. The update content includes: adjusting the geometric shape of the formation according to the seismic exploration time series data to reflect the actual formation change (such as subsidence, uplift, etc.). According to the formation attribute change distribution map, the physical properties of each layer in the three-dimensional model are updated, such as the parameters of porosity, permeability, and fluid saturation. The updated data is integrated to form the oilfield underground formation structure dynamic monitoring data, which is convenient for subsequent analysis, decision-making, and management. Visualization is provided to enable the oilfield management team to monitor the underground structure change in real time and make corresponding resource management and development plan adjustment.

[0100] As an example of the present application, reference is made toFigure 2 As shown, in the present example, the step S2 comprises:

[0101] Step S21: analyzing the underground oil and gas distribution according to the oilfield underground formation structure dynamic monitoring data, generating an underground oil and gas distribution map; calculating the total oil and gas storage amount on the underground oil and gas distribution map to obtain total oil and gas storage amount data; calculating the oil and gas production amount on the underground oil and gas distribution map according to the total oil and gas storage amount data to obtain oil and gas production amount data;

[0102] Step S22: performing data difference calculation on the total oil and gas storage amount data and the oil and gas production amount 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 value, when the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold value, then performing regional gridding 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;

[0103] Step S23: performing regional geological hardness analysis on the underground oil and gas residual regional grid through the oilfield underground formation structure dynamic monitoring data to generate grid regional geological hardness data; performing remaining oil flow path analysis on the underground oil and gas residual regional grid using the grid regional geological hardness data to generate remaining oil grid flow path data.

[0104] In the embodiments of the present application, by collecting oilfield underground formation structure dynamic monitoring data, including structure morphology, attribute change and other related geological data. Using geological modeling software (such as Petrel, GeoGraphix, etc.), the structure dynamic monitoring data is integrated with the oil and gas distribution model. The oil and gas distribution is analyzed by using the method of geostatistics (such as Kriging method, inverse distance weighted method), and the underground oil and gas distribution map is generated. The main oil and gas reservoir area, potential oil and gas migration channel and enrichment area are identified. According to the oil and gas distribution map, the total amount of oil and gas storage in each region is calculated by using the physical properties of the stratum (such as porosity, permeability, saturation, etc.). The volume method or material balance method is used to calculate the total amount of oil and gas storage, and the total amount of oil and gas storage data is obtained. According to the total amount of oil and gas storage data, combined with the production plan and historical production data, the future oil and gas production is estimated, the oil and gas production data is generated, and the predicted production at each time node is recorded. The total amount of oil and gas storage data and the oil and gas production data are calculated by difference, and the remaining oil and gas production data is generated. The specific value of the remaining oil and gas production data is determined, and the remaining recoverable oil and gas amount is recorded. The remaining oil and gas production data is compared with the preset residual oil and gas reserve threshold value. When the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold value, the next step is performed. Based on the remaining oil and gas production data, the underground oil and gas distribution map is regionally gridded, different oil and gas storage grid areas are divided, and the underground oil and gas residual area grid is generated, providing basic data for subsequent analysis. Through the oilfield underground formation structure dynamic monitoring data, the geological hardness of different regions is analyzed, and the corresponding geological hardness index (such as rock strength, stress value, etc.) is used. The regional geological hardness is analyzed by using the rock and soil mechanics model, and the grid area geological hardness data is generated. According to the grid area geological hardness data, the oil and gas flow path model is established, the geological characteristics, pressure difference and fluid mechanics principle of each region are considered, the remaining oil grid flow path data is generated, and the oil and gas flow path and potential flow channel in each grid area are recorded.

[0105] Preferably, the remaining oil and gas production data and the preset residual oil and gas reserve threshold value are compared, and when the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold value, the underground oil and gas distribution map is regionally gridded based on the remaining oil and gas production data, including:

[0106] The remaining oil and gas production data and the preset residual oil and gas reserve threshold value are compared, and when the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold value, the initial grid unit of the underground oil and gas distribution map is divided based on the remaining oil and gas production data, and the initial grid unit data is generated, wherein the initial grid unit data includes stratum attribute and oil and gas attribute;

[0107] Grid proportion calculation is performed on the formation properties and oil and gas properties to obtain formation-oil and gas grid proportion data; attribute proportion balancing is performed on the initial grid cell data according to the formation-oil and gas grid proportion data to generate internal adjustment data of the grid cell; oil and gas dynamic displacement analysis is performed on the oil and gas properties to generate oil and gas dynamic displacement data;

[0108] Grid boundary adaptive adjustment is performed on the initial grid cell data according to the oil and gas dynamic displacement data to generate external adjustment data of the grid cell; and grid numbering allocation is performed on the initial grid cell data based on the internal adjustment data of the grid cell and the external adjustment data of the grid cell to generate the underground oil and gas residual area grid.

[0109] In the embodiment of the present application, the remaining oil and gas exploitation data is compared with the preset residual oil and gas reserve threshold one by one. The area that meets the condition is determined, that is, when the remaining oil and gas exploitation data is less than or equal to the residual oil and gas reserve threshold, the subsequent step is entered. According to the remaining oil and gas exploitation data, an initial grid cell division is performed on the underground oil and gas distribution map. The initial grid cell division can adopt a uniform grid division method (such as a square or hexagonal grid), which ensures to cover the entire oil and gas distribution area and generates initial grid cell data, which includes the formation properties (such as porosity, permeability, etc.) and oil and gas properties (such as oil and gas reserves, saturation, etc.) of each grid cell. The proportion of the formation properties and the oil and gas properties in each initial grid cell is calculated to generate formation-oil and gas grid proportion data. A data analysis tool is used to analyze the proportion of different formation and oil and gas characteristics in each grid cell to ensure comprehensive evaluation. According to the formation-oil and gas grid proportion data, attribute proportion balancing is performed on the initial grid cell data. By adjusting the formation properties and the oil and gas properties in the grid cell, a reasonable ratio between the two is ensured, and internal adjustment data of the grid cell is generated. Dynamic displacement analysis is performed on the oil and gas properties, considering the flow and diffusion process of oil and gas in the formation. A fluid dynamics model or a numerical simulation method is used to generate oil and gas dynamic displacement data, which records the position change of oil and gas in each grid cell. According to the oil and gas dynamic displacement data, adaptive adjustment is performed on the grid boundary of the initial grid cell data. Considering the influence of dynamic displacement, the boundary of the grid cell is adjusted to better fit the actual oil and gas distribution, and external adjustment data of the grid cell is generated. Based on the internal adjustment data of the grid cell and the external adjustment data of the grid cell, numbering allocation is performed on the initial grid cell. A numbering system (such as regional number + unit number) is used to ensure that each grid cell has a unique identifier, which is convenient for subsequent data management and analysis. The adjusted initial grid cell data is combined with the numbering to generate a complete underground oil and gas residual area grid. The attribute information and spatial position information of each grid cell are recorded to form the final regional grid data, which provides a basis for subsequent oil and gas exploitation decision and dynamic monitoring.

[0110] Preferably, the step S23 comprises the following steps:

[0111] Step S231: Calculate the geological hardness of the grid area of the underground oil and gas residual area by the oilfield underground formation structure dynamic monitoring data, and generate grid area geological hardness data; the formula of the geological hardness calculation is as follows:

[0112]

[0113] In the formula, is the geological hardness, is the elastic modulus, is the rock density;

[0114] Step S232: Screen the low-hardness grid from the grid area of the underground oil and gas residual area according to the grid area geological hardness data, and obtain the low-hardness screening grid; analyze the geological porosity of the low-hardness screening grid, and generate low-hardness grid geological porosity data;

[0115] Step S233: Simulate the remaining oil and gas flow path of the grid area of the underground oil and gas residual area by the low-hardness grid geological porosity data, and generate oil and gas flow path simulation data;

[0116] Step S234: Visualize the oil and gas flow path simulation data to generate remaining oil grid flow path data.

[0117] In the embodiment of the application, the geological hardness is calculated by the formula, the rock density and elastic modulus data of different grid areas are obtained by the oilfield underground formation structure dynamic monitoring data. The geological hardness of each grid unit is calculated using the above formula to generate grid area geological hardness data. According to the calculated grid area geological hardness data, a low-hardness threshold (for example: H

[0118] As an example of the present application, reference is made to Figure 3As shown, the step S3 comprises:

[0119] Step S31: marking flow boundary points of the underground oil and gas residual area grid according to the remaining oil grid flow path data, to obtain area grid oil and gas flow boundary points; using the area grid oil and gas flow boundary points to splice adjacent grids of the underground oil and gas residual area grid, to generate the underground oil and gas residual area flow spliced grid;

[0120] Step S32: connecting the remaining oil grid flow path data through the underground oil and gas residual area flow spliced grid, to generate a remaining oil flow path network; calculating the oil amount aggregation according to the gravitational acceleration, to generate the oil amount flow aggregation; wherein the formula of the oil amount aggregation calculation is as shown below:

[0121]

[0122] In the formula, represents the flow oil amount, represents the oil density, represents the gravitational acceleration, represents the flow cross-sectional area, represents the flow height, represents the viscosity of the oil;

[0123] Step S33: returning the oil amount flow aggregation to the step S22 and comparing again with the preset residual oil and gas reserve threshold value, when the oil amount flow aggregation is greater than or equal to the preset residual oil and gas reserve threshold value, marking the high aggregation degree area of the underground oil and gas residual area flow spliced grid based on the oil amount flow aggregation, to generate the oil amount flow high aggregation degree area.

[0124] In the embodiment of the present application, the flow boundary points are identified and marked according to the remaining oil grid flow path data. The flow boundary points are the starting point and the ending point of the oil and gas flow in the underground oil and gas residual area grid, and are usually the turning point or the convergence point of the flow path. The area grid oil and gas flow boundary point data is generated, to record the coordinates of each boundary point and the corresponding oil and gas flow state. The adjacent grids of the underground oil and gas residual area grid are spliced using the area grid oil and gas flow boundary points. According to the position of the flow boundary points, the adjacent grids are combined to form the underground oil and gas residual area flow spliced grid. This process helps to simplify the model and improve the efficiency of subsequent analysis. The remaining oil grid flow path data is connected through the underground oil and gas residual area flow spliced grid, to form the overall network of the oil and gas flow, to generate the remaining oil flow path network data, and to record the characteristics of each connection path. The oil amount aggregation is calculated according to the gravitational acceleration, and the formula is as follows: In the formula, represents the flow oil amount, Expressed as oil density, Expressed as gravitational acceleration, Represented as the flow cross-sectional area, Represented as flow height, The viscosity of the oil is expressed as the viscosity. Using the above formula, the flow volume of oil along each path is calculated and summarized to form oil flow accumulation data. The calculated oil flow accumulation is returned to step S22 and compared again with the preset residual oil and gas reserve threshold. If the oil flow accumulation is greater than or equal to the preset residual oil and gas reserve threshold, the next step continues. Based on the oil flow accumulation, high-density areas are marked on the underground oil and gas residual area flow splicing grid. During the marking process, the boundaries, locations, and corresponding flow oil volume characteristics of the high-density areas are recorded, generating oil flow high-density area data.

[0125] As an example of the present invention, reference is made to Figure 4 As shown, step S4 in this example includes:

[0126] Step S41: Perform regional residual oil monitoring in areas with high oil flow concentration to generate residual oil monitoring data for high concentration areas;

[0127] Step S42: Based on the residual oil monitoring data of high-aggregation areas, the effective areas of high-aggregation areas of oil flow are screened to obtain the effective area data of high-aggregation residual oil.

[0128] Step S43: Analyze the residual oil distribution pattern of the effective area data with high residual oil concentration to generate residual oil distribution pattern analysis data; construct residual oil extraction strategies based on oilfield drilling data based on residual oil distribution pattern analysis data to generate residual oil extraction strategies.

[0129] In the embodiments of the present application, by arranging monitoring devices such as ground sensors and underground monitoring wells in the high-concentration area of oil flow, real-time collection of residual oil concentration and distribution information in the area is realized. The monitoring devices should have data acquisition, storage and transmission functions to ensure efficient data processing. Periodic collection of residual oil monitoring data in the area, including residual oil concentration, distribution location and time series information, generates high-concentration area residual oil monitoring data, records the characteristics and trends of residual oil at each monitoring point, and provides basic data for subsequent analysis. According to the high-concentration area residual oil monitoring data, data analysis algorithms (such as clustering analysis, statistical analysis, etc.) are applied to identify areas with high residual oil content. Set the screening threshold to determine the residual oil concentration standard of the effective area according to the historical data and real-time monitoring data. Through data analysis results, the high-concentration area of oil flow is screened for effective areas to obtain high-concentration effective area data of residual oil. Record the boundaries, locations, residual oil concentrations and other related attributes of the screened effective areas to provide a basis for subsequent remaining oil distribution analysis. Detailed analysis of the screened high-concentration effective area data of residual oil, including spatial distribution, depth distribution and time variation, etc. Apply mathematical models (such as multiple regression analysis, spatial interpolation method, etc.) to extract residual oil distribution rules to generate residual oil distribution rule analysis data. Based on the residual oil distribution rule analysis data, combined with oilfield drilling data, residual oil recovery strategies are constructed. 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.

[0130] Preferably, the residual oil high-concentration effective area data is analyzed for remaining oil distribution rules, including:

[0131] The residual oil high-concentration effective area data is analyzed for remaining oil distribution rules, including:

[0132] The residual oil high-concentration effective area data is analyzed for remaining oil distribution rules, including:

[0133] In the embodiments of the present application, the invalid data and outliers are removed by cleaning the residual oil high-concentration effective area data to ensure data quality. The data is standardized to facilitate subsequent analysis and model training. Data mining techniques are applied to extract key characteristics from the effective area data, such as residual oil concentration distribution, geological properties (such as porosity, permeability), historical production records, and oilfield formation structure, to generate residual oil characteristic data for high-concentration effective areas for subsequent model training. According to the extracted residual oil characteristic data, the data is divided into model training set and model test set according to a certain proportion (such as 70% training set, 30% test set). The characteristic distribution of the training set and the test set is ensured to be as consistent as possible to improve the generalization ability of the model. The generated model training set and model test set are saved to prepare for subsequent model training and evaluation. It is determined to use decision tree algorithm for model training because of its good interpretability and ability to handle nonlinear relationships. The parameters of the decision tree are configured, such as the maximum depth of the tree and the minimum number of sample divisions, to prevent overfitting. The model training set is used to train the decision tree algorithm to generate a residual oil distribution rule prediction pre-model. The model performance indicators (such as accuracy, recall rate, etc.) during the training process are recorded for subsequent optimization. The model test set is used to evaluate the residual oil distribution rule prediction pre-model, and the performance indicators (such as accuracy, F1 score, etc.) of the model are calculated. According to the evaluation results, the parameters of the decision tree model are adjusted for model optimization iteration. Cross-validation method can be used to ensure the stability of the model. The training and evaluation process is repeated until the satisfactory model performance indicators are achieved, and the final residual oil distribution rule prediction model is generated. The residual oil high-concentration effective area data is imported into the finally generated residual oil distribution rule prediction model. The model is used to analyze the residual oil distribution rule to obtain the prediction results of the residual oil distribution in the area, generate residual oil distribution rule analysis data, including residual oil distribution map, concentration prediction and other key characteristics. The analysis results are arranged into a report and provided to the oilfield management department to support subsequent production decision-making and strategy formulation.

[0134] The beneficial effects of the present application are that by obtaining oilfield exploitation location information data and oilfield drilling data, the basic integrity of the data is ensured, providing accurate geographical and technical background for subsequent analysis. By collecting standard seismic wave reflection signals, the stratigraphic structure of the oilfield can be clearly identified, promoting more accurate underground imaging. Real-time dynamic monitoring of the geological structure changes of the oilfield helps to timely adjust the exploitation strategy and optimize resource allocation. The underground oil and gas distribution map generated based on dynamic monitoring data can provide spatial distribution information of oil and gas, helping to identify oil and gas enrichment areas. Calculating the exploitation data of remaining oil and gas helps to evaluate the development potential and economic benefits of the oilfield. The underground oil and gas distribution map is regionally gridded, which can refine the analysis and make the subsequent flow path analysis more accurate. Clearly defining the flow path of remaining oil helps to optimize the exploitation strategy and improve the recovery rate of remaining oil. By splicing adjacent grids, the data of different regions can be integrated to form a more comprehensive residual oil and gas region flow splicing grid. Accurate calculation of oil flow aggregation provides a basis for subsequent high aggregation area identification. Marking high aggregation areas helps to quickly locate the most valuable development areas and improve exploitation efficiency. By screening oil flow high aggregation areas, the efficiency of development resources is ensured, and invalid exploitation is avoided. Analyzing the high aggregation effective area can reveal the distribution characteristics of remaining oil and optimize subsequent exploitation strategies. The residual oil exploitation strategy based on analysis data makes the exploitation process more scientific and systematic. Therefore, the present application improves the accuracy and practicality of remaining oil distribution rule analysis through dynamic monitoring, refined oil and gas distribution analysis, efficient flow path identification and systematic exploitation strategy construction.

[0135] Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the description given above, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

[0136] The above description is merely that of a specific implementation of the present application, which enables those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application shall not be limited to these embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing a remaining oil distribution profile, characterized by, The method comprises the following steps: Step S1: obtaining oilfield exploitation position information data and oilfield drilling data; collecting seismic wave reflection signals according to the oilfield exploitation position information data to obtain standard seismic wave reflection signals; and performing four-dimensional geological structure dynamic monitoring on the standard seismic wave reflection signals by using the oilfield drilling data, thereby generating oilfield underground formation structure dynamic monitoring data; Step S2: analyzing underground oil and gas distribution according to the oilfield underground formation structure dynamic monitoring data to generate an underground oil and gas distribution map; calculating a difference value of remaining oil and gas exploitation on the underground oil and gas distribution map to generate remaining oil and gas exploitation data; performing regional gridding on the underground oil and gas distribution map based on the remaining oil and gas exploitation data to generate an underground oil and gas residual regional grid; performing remaining oil flow path analysis on the underground oil and gas residual regional grid to generate remaining oil grid flow path data; The remaining oil flow path analysis on the underground oil and gas residual regional grid to generate remaining oil grid flow path data specifically comprises: Step S231: calculating the geological hardness of the underground oil and gas residual regional grid by using the oilfield underground formation structure dynamic monitoring data to generate grid area geological hardness data; wherein the formula of the geological hardness calculation is as follows: wherein, expressed as geologic hardness, expressed as elastic modulus, expressed as rock density; Step S232: screening low-hardness grids from the underground oil and gas residual regional grid according to the grid area geological hardness data to obtain low-hardness screening grids; and 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 regional grid by using the low-hardness grid geological porosity data to generate oil and gas flow path simulation data; Step S234: visualizing the oil and gas flow path simulation data to generate remaining oil grid flow path data; Step S3: splicing adjacent grids from the underground oil and gas residual regional grid according to the remaining oil grid flow path data to generate an underground oil and gas residual regional flow splicing grid; calculating oil flow aggregation from the underground oil and gas residual regional flow splicing grid to generate oil flow aggregation; and marking high aggregation degree areas from the underground oil and gas residual regional flow splicing grid by using the oil flow aggregation to generate an oil flow high aggregation degree area; Step S4: screening effective areas from the oil flow high aggregation degree area to obtain residual oil high aggregation degree effective area data; analyzing the residual oil distribution law from the residual oil high aggregation degree effective area data to generate residual oil distribution law analysis data; and constructing a residual oil exploitation strategy from the oilfield drilling data based on the residual oil distribution law analysis data, thereby generating a residual oil exploitation strategy.

2. The method of residual oil distribution analysis according to claim 1, wherein, Step S1 comprises the following steps: Step S11: obtaining oilfield exploitation position information data and oilfield drilling data; Step S12: transmitting seismic waves according to the oilfield exploitation position information data and synchronously recording to obtain seismic wave reflection signals; and performing signal preprocessing on the seismic wave reflection signals to generate standard seismic wave reflection signals, wherein the signal preprocessing comprises signal denoising, signal normalization and signal detrending processing; Step S13: three-dimensional oilfield underground formation modeling is performed on the standard seismic wave reflection signal by using the oilfield drilling data, and three-dimensional oilfield underground formation modeling data is generated; Step S14: four-dimensional geological structure dynamic monitoring is performed on the three-dimensional oilfield underground formation modeling data, and oilfield underground formation structure dynamic monitoring data is generated.

3. The method of residual oil distribution analysis according to claim 2, wherein, Step S13 includes the following steps: logging velocity analysis is performed on the oilfield drilling data to generate oilfield logging velocity data; seismic velocity inversion is performed on the standard seismic wave reflection signal according to the oilfield logging velocity data to generate oilfield regional seismic wave velocity data; reflection wave travel time picking is performed on the standard seismic wave reflection signal to generate seismic reflection wave trend point data; depth linear conversion is performed on the seismic reflection wave trend point data and the oilfield regional seismic wave velocity data to generate geological layer depth information data; Fourier transform is performed on the standard seismic wave reflection signal to generate seismic wave reflection spectrum data; reflection interface identification is performed on the seismic wave reflection spectrum data according to the geological layer depth information data to obtain underground layer geological interface data; stratum attribute information extraction is performed on the underground layer geological interface data to obtain stratum attribute information data; reflection interface-based horizon modeling is performed based on the underground layer geological interface data to generate three-dimensional stratum structure framework data; initial stratum attribute assignment is performed on the three-dimensional stratum structure framework data by using the stratum attribute information data to generate the three-dimensional oilfield underground formation modeling data.

4. The method of residual oil distribution analysis according to claim 2, wherein, Step S14 includes the following steps: Step S141: time reference point confirmation is performed on the three-dimensional oilfield underground formation modeling data to obtain monitoring time node data; repeated seismic exploration data acquisition is performed on the three-dimensional oilfield underground formation modeling data based on the monitoring node time data to obtain a seismic exploration time series data set; Step S142: seismic data difference analysis is performed on the seismic exploration time series data set to generate a time difference stratum change profile; stratum attribute comparison analysis is performed on the time difference stratum change profile to generate a stratum attribute change distribution map; Step S143: dynamic geological data updating is performed on the three-dimensional oilfield underground formation modeling data by using the stratum attribute change map to generate the oilfield underground formation structure dynamic monitoring data, wherein the dynamic geological data updating includes structure form dynamic updating and attribute dynamic updating.

5. The method of residual oil distribution analysis according to claim 1, wherein, Step S2 includes the following steps: Step S21: underground oil and gas distribution analysis is performed on the oilfield underground formation structure dynamic monitoring data to generate an underground oil and gas distribution map; oil and gas storage total amount calculation is performed on the underground oil and gas distribution map to obtain oil and gas storage total amount data; oil and gas production amount calculation is performed on the underground oil and gas distribution map according to the oil and gas storage total amount data to obtain oil and gas production amount data; Step S22: data difference calculation is performed on the oil and gas storage total amount data and the oil and gas production amount data to generate remaining oil and gas production data; comparison is performed between the remaining oil and gas production data and a preset residual oil and gas reserve threshold value; when the remaining oil and gas production data is less than or equal to the residual oil and gas reserve threshold value, 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 region grid. Step S23: Analyzing the regional geological hardness of the underground oil and gas residual area grid through the oilfield subsurface formation structure dynamic monitoring data to generate grid regional geological hardness data; using 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.

6. The method of residual oil distribution analysis according to claim 5, wherein, The remaining oil and gas exploitation data is compared with the preset residual oil and gas reserve threshold value, and when the remaining oil and gas exploitation data is less than or equal to the residual oil and gas reserve threshold value, the underground oil and gas distribution map is regionally gridded based on the remaining oil and gas exploitation data, including: The remaining oil and gas exploitation data is compared with the preset residual oil and gas reserve threshold value, and when the remaining oil and gas exploitation data is less than or equal to the residual oil and gas reserve threshold value, the initial grid cell data is generated by initially dividing the underground oil and gas distribution map into grid cells based on the remaining oil and gas exploitation data, wherein the initial grid cell data includes formation attributes and oil and gas attributes; The grid proportion of the formation attributes and the oil and gas attributes is calculated to obtain formation-oil and gas grid proportion data; the attribute proportion balance of the initial grid cell data is performed according to the formation-oil and gas grid proportion data to generate grid cell internal adjustment data; the oil and gas dynamic displacement analysis of the oil and gas attributes is performed to generate oil and gas dynamic displacement data; The grid boundary self-adaptive adjustment of the initial grid cell data is performed according to the oil and gas dynamic displacement data to generate grid cell external adjustment data; the initial grid cell data is numbered and distributed based on the grid cell internal adjustment data and the grid cell external adjustment data to generate the underground oil and gas residual area grid.

7. The method of residual oil distribution analysis according to claim 1, wherein, Step S3 includes the following steps: Step S31: Marking the flow boundary points of the underground oil and gas residual area grid according to the remaining oil grid flow path data to obtain 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 area grid to generate the underground oil and gas residual area flow splicing grid; Step S32: Connecting 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; calculating the oil quantity aggregation of the remaining oil flow path network according to the gravitational acceleration to generate an oil quantity flow aggregation; wherein the formula of the oil quantity aggregation calculation is as follows: wherein is expressed as the flow oil amount, is expressed as the oil density, is expressed as the gravitational acceleration, is expressed as the flow cross-sectional area, is expressed as the flow height, is expressed as the viscosity of the oil; Step S33: Comparing the oil quantity flow aggregation with the preset residual oil and gas reserve threshold value again in step S22, and when the oil quantity flow aggregation is greater than or equal to the preset residual oil and gas reserve threshold value, marking the high aggregation degree area of the underground oil and gas residual area flow splicing grid based on the oil quantity flow aggregation to generate the oil quantity flow high aggregation degree area.

8. The method for analyzing the distribution of remaining oil according to claim 1, wherein, Step S4 includes the following steps: Step S41: Monitoring the residual oil of the oil quantity flow high aggregation degree area to generate high aggregation degree area residual oil monitoring data; Step S42: Screening the effective area of the oil quantity flow high aggregation degree area according to the high aggregation degree area residual oil monitoring data to obtain residual oil high aggregation degree effective area data; Step S43: residual oil distribution rule analysis is performed on the residual oil high accumulation degree effective area data, residual oil distribution rule analysis data is generated, residual oil exploitation strategies are constructed based on the residual oil distribution rule analysis data and oilfield drilling data, and residual oil exploitation strategies are generated.

9. The method of residual oil distribution analysis according to claim 8, wherein, The residual oil distribution rule analysis on the residual oil high accumulation degree effective area data includes: The residual oil high accumulation degree effective area data is subjected to regional residual oil characteristic extraction, and high accumulation degree effective area residual oil characteristic data is obtained. The high accumulation degree effective area residual oil characteristic data is subjected to data set division, and a model training set and a model test set are generated. A residual oil distribution rule prediction pre-model is generated by performing model training on the model training set through a decision tree algorithm. The residual oil distribution rule prediction pre-model is subjected to model optimization iteration according to the model test set, and a residual oil distribution rule prediction model is generated. The residual oil high accumulation degree effective area data is imported into the residual oil distribution rule prediction model for residual oil distribution rule analysis, and residual oil distribution rule analysis data is generated.

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