Well location collaborative deployment method based on dynamic oil reservoir model and related equipment

Abstract

The invention relates to a well location collaborative deployment method based on a dynamic oil reservoir model and related equipment, and the method comprises the steps: obtaining a plurality of digital-analog models corresponding to a target oil and gas field and oil field related data, and carrying out historical fitting based on the digital-analog models and the oil field related data to obtain corresponding historical fitting data; establishing a linkage relationship between the digital-analog models, and obtaining a target area based on the historical fitting data and the linkage relationship; obtaining an initial well location based on the target area and generating a well trajectory corresponding to the initial well location to obtain new well information corresponding to the initial well location; and S4, generating a corresponding new well production system based on the new well information, and obtaining corresponding new well predicted productivity according to the new well production system and the historical fitting data, so as to obtain a corresponding well location deployment result by taking the new well information corresponding to the initial well location as target well location information when the new well predicted productivity meets a preset target. By implementing the method, the complexity of a well location collaborative deployment process can be reduced, and rapid well location collaborative deployment is realized.

Description

Technical Field

[0001] This invention relates to the field of oilfield analysis technology, and more specifically, to a well location collaborative deployment method and related equipment based on a dynamic reservoir model. Background Technology

[0002] Well placement is a well design method that determines the location of production wells based on a series of processes including geological research, remaining oil analysis, and production capacity prediction. It requires coordination among geologists, reservoir engineers, and even drilling designers. Given the high demands of the research process and personnel coordination, rapid well placement is challenging. In the specific implementation of well placement, it typically relies on expert experience and judgment. While the experience-based method plays an important role in exploration practice, it is inherently weak due to significant differences in individual expert subjective judgment, limitations in knowledge structure, and limited human resources, making it difficult to achieve objective optimization of the placement plan. The core of traditional placement methods lies in the geological understanding of underground oil and gas enrichment patterns and occurrence states, following the basic laws of exploration geology. To improve the scientific rigor of exploration well placement, domestic and international scholars have conducted extensive research on reserve prediction and exploration laws, resulting in a series of representative models such as the Weng's life cycle model, the Logistic production change model, grey system theory, resource-reserve-production prediction models, reservoir distribution probability models, fractal mathematical methods, the Gompattz model, the reservoir scale sequence method, the "broom" model, the reserve growth coefficient method, and geological analysis methods. However, these models mostly focus on macro-level reserve growth trend prediction and generally suffer from problems such as complex formulas, stringent applicable conditions, and cumbersome calculation processes. They are poorly adaptable to exploration areas with complex geological structures and strong heterogeneity, making it difficult to effectively guide the precise deployment of actual exploration well locations. Therefore, how to organically integrate multi-source information such as geological laws, exploration knowledge, well distribution, reservoir characteristics, and reserve growth dynamics to construct a simple and highly applicable exploration well location deployment planning model, and based on this, to form the optimal well location deployment scheme, has become a key link in improving the success rate and exploration benefits of exploration wells under current complex geological conditions. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a well location collaborative deployment method and related equipment based on a dynamic reservoir model, addressing the aforementioned technical deficiencies of the prior art.

[0004] The technical solution adopted by this invention to solve its technical problem is: constructing a well location collaborative deployment method based on a dynamic reservoir model, the method comprising: S1. Obtain several numerical modeling models corresponding to the target oil and gas field and oil field-related data, and perform historical fitting based on the numerical modeling models and oil field-related data to obtain corresponding historical fitting data. S2. Establish the linkage relationship between the numerical models, and conduct geological research comparison and remaining oil analysis based on the historical fitting data and the linkage relationship to obtain the target area in the target oil and gas field; S3. Based on the target area, obtain the initial well location and generate the well trajectory corresponding to the initial well location to obtain the new well information corresponding to the initial well location; S4. Generate a corresponding new well production system based on the new well information, and obtain the corresponding new well predicted production capacity according to the new well production system and the historical fitting data, so that when the new well predicted production capacity meets the preset target, the new well information corresponding to the initial well position is used as the target well position information to obtain the corresponding well position deployment result.

[0005] Preferably, in one embodiment of the well location collaborative deployment method based on dynamic reservoir model of the present invention, the numerical model includes a numerical static model obtained by coarsening the fine geological model corresponding to the target oil and gas field and a numerical dynamic remaining oil model, wherein the numerical static model includes a structural model, a net-to-gross ratio model, a permeability model, and a porosity model; the oil field related data includes relative permeability curve data, high pressure physical property data, equilibrium data, and production system data corresponding to the target oil and gas field; The step of performing historical fitting based on the numerical model and the oilfield-related data to obtain corresponding historical fitting data includes: The reserve and production regime data of the numerical model are fitted by adjusting the porosity model, the permeability model, the relative permeability curve data, the high pressure property data, and the equilibrium data.

[0006] Preferably, in one embodiment of the well location collaborative deployment method based on dynamic reservoir model according to the present invention, in step S2, establishing the linkage relationship between the numerical models includes: A multi-dimensional visualization linkage is established between the refined geological model, the static numerical model, and the dynamic residual oil model; wherein, the multi-dimensional visualization linkage includes the linkage of the three-dimensional, two-dimensional plane, and two-dimensional profile windows of each model.

[0007] Preferably, in one embodiment of the well location collaborative deployment method based on a dynamic reservoir model according to the present invention, in step S2, the geological research comparison and remaining oil analysis based on the historical fitting data and the linkage relationship to obtain the target area in the target oil and gas field includes: The target area is obtained based on the remaining oil saturation, reserve abundance, streamline sweep extent, reservoir properties, and structural location.

[0008] Preferably, in one embodiment of the well location collaborative deployment method based on a dynamic reservoir model according to the present invention, it further includes: Based on the multi-dimensional visualization linkage, the remaining oil of the dynamic remaining oil model, the permeability of the permeability model, and the construction of the construction model are obtained based on the same location, so as to analyze the remaining oil enrichment area as the target area.

[0009] Preferably, in one embodiment of the well location collaborative deployment method based on a dynamic reservoir model according to the present invention, in step S3, obtaining the initial well location based on the target area and generating the well trajectory corresponding to the initial well location to obtain the new well information corresponding to the initial well location includes: The user triggers the addition of an identifier to simultaneously generate the new well information in all said digital modeling models, wherein the well trajectory corresponding to the initial well location passes through all grids of said digital modeling models.

[0010] Preferably, in one embodiment of the well location collaborative deployment method based on a dynamic reservoir model according to the present invention, in step S4, obtaining the corresponding predicted production capacity of the new well based on the new well production regime and the historical fitting data includes: Based on the historical fitting data, the production indicators of new wells at the historical fitting end time and prediction time stage are obtained, and the prediction numerical model results are obtained based on the new well production indicators and the dynamic remaining oil model.

[0011] The present invention also provides an electronic device comprising: a module for performing the method described above.

[0012] The present invention also provides an electronic device, including a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program to implement the method described above.

[0013] The present invention also provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described above.

[0014] Implementing the well location collaborative deployment method and related equipment based on a dynamic reservoir model of the present invention has the following beneficial effects: it can reduce the complexity of the well location collaborative deployment process and achieve rapid well location collaborative deployment. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of an embodiment of the well location collaborative deployment method based on a dynamic reservoir model of the present invention; Figure 2 This is a schematic diagram of a dynamic remaining oil model, representing an embodiment of the well location collaborative deployment method based on a dynamic reservoir model according to the present invention. Figure 3 This is a schematic diagram of the remaining oil plane distribution in an embodiment of the well location collaborative deployment method based on a dynamic reservoir model according to the present invention; Figure 4 This is a schematic diagram of the well trajectory of an embodiment of the well location collaborative deployment method based on a dynamic reservoir model of the present invention; Figure 5 This is a prediction curve of new well production indicators based on an embodiment of the well location collaborative deployment method based on a dynamic reservoir model according to the present invention. Detailed Implementation

[0016] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0017] like Figure 1 As shown, an embodiment of the well location collaborative deployment method based on a dynamic reservoir model of the present invention is illustrated. Figure 1 In one embodiment of the well location collaborative deployment method based on a dynamic reservoir model of the present invention, the following steps are included: S1, acquiring several numerical models corresponding to the target oil and gas field and oil field-related data, and performing historical fitting based on the several numerical models and the oil field-related data to obtain corresponding historical fitting data; S2, establishing the linkage relationship between the numerical models, and performing geological research comparison and remaining oil analysis research based on the historical fitting data and the linkage relationship to obtain the target area in the target oil and gas field; S3, obtaining the initial well location based on the target area and generating the well trajectory corresponding to the initial well location to obtain the new well information corresponding to the initial well location; S4, generating the corresponding new well production system based on the new well information, and obtaining the corresponding new well predicted production capacity according to the new well production system and the historical fitting data, so that when the new well predicted production capacity meets the preset target, the new well information corresponding to the initial well location is used as the target well location information to obtain the corresponding well location deployment result.

[0018] Based on step S1, when deploying wells to a target oil and gas field, it is first necessary to obtain the numerical model corresponding to the target oil and gas field, as well as the relevant oil field data. This relevant data can be understood as the historical data of the target oil and gas field. This process can be understood as the data and functional system preparation stage for the numerical model scheme, that is, preparing a numerical model scheme for the target oil and gas field with completed historical data fitting through this process. Multiple numerical models can be used to analyze different characteristics of the target oil and gas field. The process of obtaining historical data fitting can also be understood as analyzing different data based on different numerical models to obtain the historical data analysis results for each numerical model.

[0019] Based on step S2, geological research comparison and remaining oil analysis are conducted based on the linkage relationship between various numerical models and the historical fitting data of each numerical model to obtain the target area in the target oil and gas field.

[0020] Based on steps S3 and S4, after obtaining the target area, an initial well location can be set according to the target area, and the well trajectory of the initial well location can be set according to the geographical location, ultimately obtaining the new well information for the initial well location. After obtaining the relevant information of the new well, the production system of the new well can be formulated, and then the production capacity of the new well can be predicted to determine whether the predicted production capacity of the new well can meet the preset requirements. Only when the preset production capacity of the new well meets the preset requirements can the location of the initial well be considered ideal, thus completing the entire well location deployment process. In one embodiment, if the predicted production capacity of the new well cannot meet the requirements, it may be necessary to re-analyze based on historical fitting data to obtain new well location information, so as to find the most suitable well location through this process.

[0021] In one embodiment, the numerical model includes a static numerical model and a dynamic numerical model of remaining oil obtained by coarsening the fine geological model corresponding to the target oil and gas field. The static numerical model includes a structural model, a net-to-gross ratio model, a permeability model, and a porosity model. The oil field-related data includes relative permeability curve data, high-pressure physical property data, equilibrium data, and production regime data corresponding to the target oil and gas field. The step of performing historical fitting based on the numerical model and the oil field-related data to obtain corresponding historical fitting data includes: fitting the reserves and production regime data of the numerical model by adjusting the porosity model, the permeability model, the relative permeability curve data, the high-pressure physical property data, and the equilibrium data.

[0022] Specifically, among the several numerical simulation models corresponding to the target oil and gas field, the refined geological model is used to represent the field's attributes, including lithofacies, net-to-gross ratio, porosity, permeability, and water saturation. The static numerical simulation model is derived from this refined geological model. The coarsened static model then performs corresponding data analysis using structural models, net-to-gross ratio models, permeability models, and porosity models. The dynamic remaining oil model represents the remaining oil at different time points in the numerical simulation calculations. Each model can be understood as derived from a commonly used model design in the current oil and gas field. The dynamic remaining oil model requires preprocessing settings for the numerical simulation simulator, such as black oil simulator settings, start and end times, relative permeability curves, high-pressure properties, equilibrium data, and production regime settings. These are essential for the numerical simulation scheme and provide support for subsequent numerical simulation steps. The acquired oilfield-related data can be common data used in oil and gas fields, such as relative permeability curve data, high-pressure property data, equilibrium data, and production regime data. This data can be recorded according to the oil and gas field production process.

[0023] In the specific fitting process, the reserve and production regime data of the numerical model are fitted by adjusting the porosity model and permeability model in the static numerical model, as well as the relative permeability curve data, high-pressure physical property data, and equilibrium data in the oilfield-related data. The reserves in the numerical model are the crude oil volume in the model, and the production regime data include the daily oil production, water cut, and bottom hole flowing pressure data of a single well. The equilibrium data, production regime data, as well as the aforementioned relative permeability curve data and high-pressure physical property data, are all data required for the preprocessing of the numerical model simulation. The equilibrium data are the oil-water interface and oil-gas interface data, and the production regime data are the well's production regime data, including daily oil production, water cut, and bottom hole flowing pressure. It can be understood that the above-mentioned reserve and production regime data are the objects or targets of historical fitting.

[0024] In one embodiment, in step S2, establishing the linkage relationship between the digital models includes: establishing a multi-dimensional visualization linkage between the detailed geological model, the static digital model, and the dynamic residual oil model; wherein, the multi-dimensional visualization linkage includes the linkage of the three-dimensional, two-dimensional planar, and two-dimensional profile windows of each model. Specifically, the linkage relationship between multiple digital models mainly involves the multi-dimensional visualization linkage between the detailed geological model, the static digital model, and the dynamic residual oil model. All models within all windows synchronously perform rotation, translation, and scaling operations at the same position and orientation; that is, rotating, translating, or scaling a model in only one window allows for synchronous rotation, translation, and scaling operations of the corresponding models in other windows at the same position and orientation. The multi-dimensional visualization linkage can be the linkage of three-dimensional, two-dimensional planar, and two-dimensional profile windows. The detailed geological model, the static digital model, and the dynamic residual oil model can be displayed simultaneously through a display window. When an operation is performed on a single model, the corresponding model will respond accordingly due to the linkage relationship.

[0025] In one embodiment, in step S2, the geological research comparison and residual oil analysis based on the historical fitting data and the linkage relationship to obtain the target area in the target oil and gas field includes: obtaining the target area based on residual oil saturation, reserve abundance, streamline sweep degree, reservoir properties, and structural location. Specifically, in the process of obtaining the target area, areas with high residual oil saturation and large reserve abundance, areas not swept by streamlines or with low sweep degree, areas with relatively poor reservoir properties leading to residual oil enrichment caused by injected water bypassing, and high structural areas are selected as the target areas.

[0026] In one embodiment, the well location collaborative deployment method based on a dynamic reservoir model of the present invention further includes: based on the multi-dimensional visualization linkage, obtaining the remaining oil of the dynamic remaining oil model, the permeability of the permeability model, and the structure of the structural model based on the same location, so as to analyze the remaining oil enrichment area as the target area. Specifically, based on the dynamic remaining oil saturation model, streamline model, permeability model, and structural model of the visualization multi-window linkage, the streamline sweep, reservoir properties, and structural location of the remaining oil enrichment area can be analyzed to determine the range with high remaining oil saturation, low sweep, good reservoir properties, and favorable structural location, thereby determining the potential tapping range.

[0027] In one embodiment, in step S3, obtaining the initial well location based on the target area and generating the well trajectory corresponding to the initial well location to obtain the new well information corresponding to the initial well location includes: adding an identifier triggered by the user to simultaneously generate the new well information in all the digital model models, wherein the well trajectory corresponding to the initial well location passes through all grids of the digital model model. Specifically, the one-click add injection-production well function allows users to click on the digital model grid within the potential tapping range in the model window viewing mode, right-click to expand the model function menu, select add new well, and select the required well type, including oil production well, gas production well, and water injection well. After selecting the well type, the well trajectory of the new well is automatically generated on the vertical path of the grid, and the well trajectory is pushed to the digital model scheme production system. At the same time, the well location and well trajectory of the new well are displayed on the digital model model.

[0028] In one embodiment, in step S4, obtaining the corresponding predicted production capacity of the new well based on the new well production regime and the historical fitting data includes: obtaining the new well production index for the historical fitting end time and prediction time stage based on the historical fitting data, and obtaining the prediction numerical model result based on the new well production index and the dynamic remaining oil model. Specifically, the new well production regime configuration mainly includes the setting of perforation grid, production regime, and prediction period. The perforation grid position is the production layer, and the default is all perforations, i.e., full-layer production, but the production layer needs to be adjusted according to production needs. Production capacity includes fixed pressure or fixed production volume production, expected bottomhole flow pressure or production volume, characterizing the expected production capacity. The prediction production period indicates the length of time the prediction result spans. The production regime is determined by setting the production layer, production capacity, and prediction time. Based on different embodiments, the same or similar perforation layers and production capacities are configured according to the production regime parameters of adjacent wells, and the prediction period is set based on the planned period of well location deployment needs. The content of the new well production regime configuration is determined hierarchically, thereby completing the formulation of the numerical model prediction scheme for the new well. After obtaining the production system configuration for the new well, the production indicators and dynamic remaining oil model for the period from the restart time (i.e., the end time of historical fitting) to the prediction time are calculated through numerical simulation. The production indicators and dynamic remaining oil model of the new well at different time points in this period are the prediction numerical simulation results. By using the prediction numerical simulation results indicators such as oil production, water production, cumulative production, and bottom hole pressure, the production capacity of the new well is comprehensively evaluated to determine whether it has reached the expected cumulative production and pressure maintenance level, thereby confirming the effectiveness of the well location deployment.

[0029] In one specific embodiment, the prepared detailed geological model of oilfield M includes property models such as lithofacies, net-to-gross ratio, porosity, permeability, and water saturation. The numerical modeling scheme includes a coarse static numerical model and a dynamic remaining oil model, as well as scheme settings. The numerical modeling scheme has completed historical fitting and can be used for predictive scheme research. It has been integrated with a system that integrates model visualization and linkage functions, the function of quickly adding new wells and pushing production regimes, and numerical simulation and post-processing functions. Specifically, the prepared dynamic remaining oil model is as follows: Figure 2 As shown.

[0030] In the stage of determining the potential area through multi-dimensional visualization linkage of models, multi-dimensional visualization linkage is based on static refined geological attribute models (lithology, porosity, and permeability), static attributes of numerical models, and dynamic remaining oil models. This multi-dimensional visualization linkage includes multi-dimensional visualization linkage of model data such as three-dimensional, two-dimensional plane, and two-dimensional profile. Geological and reservoir researchers conduct geological research comparisons and remaining oil analysis to determine the potential area. Figure 3 The remaining oil distribution map of the main reservoir shown is used to determine the potential for further exploitation.

[0031] In the stage of quickly adding and drawing well trajectories for new wells, the one-click function of adding injection and production wells via the right-click menu based on the digital model allows for the rapid addition of new well locations and the drawing of well trajectories within the potential tapping range. This adds new well information to the digital model scheme, and the well trajectory is designed based on the remaining oil profile, such as... Figure 4 As shown.

[0032] In the new well coordinate data push production system phase, a default perforation section is generated based on the new well trajectory and the numerical model compared with the grid. The default perforation data of the new well is then pushed to the numerical model scheme production system, and the required new well working system is set to complete the configuration of the numerical model prediction scheme for the new well.

[0033] In the numerical simulation and post-processing capacity expectation evaluation stage, based on the numerical simulation simulator and post-processing functions, the numerical simulation prediction scheme for the new well is calculated and the post-processing index data is displayed. The predicted capacity of the new well is evaluated to see if it meets production expectations, thus achieving the goal of well location coordinated deployment. This completes the well location coordinated deployment workflow. The production index prediction curve for the new well is shown below. Figure 5 As shown.

[0034] Furthermore, an electronic device of the present invention has the function of implementing the corresponding steps performed in the above-described method. Each function can be implemented by hardware or by hardware executing corresponding software. The corresponding hardware or software includes one or more modules corresponding to the above-described functions. That is, the steps in the above-described method are executed by one or more modules respectively. The specific cooperative operation between the modules can be referred to the specific process of the above-described method, and will not be repeated here.

[0035] Furthermore, an electronic device according to the present invention may further include a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the above-described method. Specifically, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, when the computer program is downloaded, installed, and executed by an electronic device, it performs the functions defined in the methods of the embodiments of the present invention. The electronic device in the present invention may be a terminal such as a laptop, desktop computer, tablet computer, or smartphone, or it may be a server.

[0036] Furthermore, this invention provides a computer storage medium storing a computer program, which, when executed by a processor, implements any of the methods described above. Specifically, it should be noted that the computer-readable medium described above can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0037] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0038] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A well location collaborative deployment method based on a dynamic reservoir model, characterized in that, The method includes: S1. Obtain several numerical modeling models corresponding to the target oil and gas field and oil field-related data, and perform historical fitting based on the numerical modeling models and oil field-related data to obtain corresponding historical fitting data. S2. Establish the linkage relationship between the numerical models, and conduct geological research comparison and remaining oil analysis based on the historical fitting data and the linkage relationship to obtain the target area in the target oil and gas field; S3. Based on the target area, obtain the initial well location and generate the well trajectory corresponding to the initial well location to obtain the new well information corresponding to the initial well location; S4. Generate a corresponding new well production system based on the new well information, and obtain the corresponding new well predicted production capacity according to the new well production system and the historical fitting data, so that when the new well predicted production capacity meets the preset target, the new well information corresponding to the initial well position is used as the target well position information to obtain the corresponding well position deployment result.

2. The well location collaborative deployment method based on a dynamic reservoir model according to claim 1, characterized in that, The numerical model includes a static numerical model and a dynamic numerical model of remaining oil, obtained by coarsening the fine geological model corresponding to the target oil and gas field. The static numerical model includes a structural model, a net-to-gross ratio model, a permeability model, and a porosity model. The oil field-related data includes relative permeability curve data, high-pressure physical property data, equilibrium data, and production system data corresponding to the target oil and gas field. The step of performing historical fitting based on the numerical model and the oilfield-related data to obtain corresponding historical fitting data includes: The reserve and production regime data of the numerical model are fitted by adjusting the porosity model, the permeability model, the relative permeability curve data, the high pressure property data, and the equilibrium data.

3. The well location collaborative deployment method based on a dynamic reservoir model according to claim 2, characterized in that, In step S2, establishing the linkage relationship between the digital models includes: A multi-dimensional visualization linkage is established between the refined geological model, the static numerical model, and the dynamic residual oil model; wherein, the multi-dimensional visualization linkage includes the linkage of the three-dimensional, two-dimensional plane, and two-dimensional profile windows of each model.

4. The well location collaborative deployment method based on a dynamic reservoir model according to claim 2, characterized in that, In step S2, the geological research comparison and remaining oil analysis based on the historical fitting data and the linkage relationship to obtain the target area in the target oil and gas field includes: The target area is obtained based on the remaining oil saturation, reserve abundance, streamline sweep extent, reservoir properties, and structural location.

5. The well location collaborative deployment method based on a dynamic reservoir model according to claim 5, characterized in that, The method further includes: Based on the multi-dimensional visualization linkage, the remaining oil of the dynamic remaining oil model, the permeability of the permeability model, and the construction of the construction model are obtained based on the same location, so as to analyze the remaining oil enrichment area as the target area.

6. The well location collaborative deployment method based on a dynamic reservoir model according to claim 1, characterized in that, In step S3, obtaining the initial well location based on the target area and generating the well trajectory corresponding to the initial well location to obtain the new well information corresponding to the initial well location includes: The user triggers the addition of an identifier to simultaneously generate the new well information in all said digital modeling models, wherein the well trajectory corresponding to the initial well location passes through all grids of said digital modeling models.

7. The well location collaborative deployment method based on a dynamic reservoir model according to claim 1, characterized in that, In step S4, obtaining the corresponding predicted production capacity of the new well based on the new well production system and the historical fitting data includes: Based on the historical fitting data, the production indicators of new wells at the historical fitting end time and prediction time stage are obtained, and the prediction numerical model results are obtained based on the new well production indicators and the dynamic remaining oil model.

8. An electronic device, characterized in that, The electronic device includes a module for performing the method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is used to execute the computer program to implement the method as described in any one of claims 1 to 7.

10. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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