Method, system and equipment for optimizing carbon dioxide flooding miscible phase degree
By obtaining and analyzing the injection and production parameters of carbon dioxide oil flooding and well network parameters, using preset agent models to determine the phase state parameters, and determining the regulation and optimization strategy based on these parameters, the problem of optimization of the phase mixing degree of carbon dioxide oil flooding in the existing technology is solved, and the reliability and accuracy of the oil flooding effect are improved.
Patent Information
- Application Number
- CN202311396753.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-25
- Publication Date
- 2025-04-25
AI Technical Summary
The prior art is difficult to effectively improve the degree of phase mixing of carbon dioxide oil flooding, especially during injection and production, and it is difficult to ensure the pressure level and phase mixing capacity between injection and production wells.
By obtaining the injection and production parameters and well network parameters before and after carbon dioxide oil flooding, the phase state parameters are determined using the preset agent model, including the mixed-phase degree parameters and the mixed-phase driving state parameters. According to these parameters and models, the regulation and optimization strategy is determined, and the injection and production parameters and well network parameters are adjusted.
The optimization reliability and accuracy of carbon dioxide oil-drifting mixing degree is improved, and a scientific basis is provided for improving the CO2 oil-drifting effect of land-phase reservoirs.
Smart Images
Figure CN119885518A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of oil reservoir recovery, and in particular to a method for optimizing the miscibility of carbon dioxide oil recovery, a system for optimizing the miscibility of carbon dioxide oil recovery, an electronic device and a readable storage medium. Background Art
[0002] At present, CO2 injection and oil recovery technology has attracted widespread attention because it can achieve a win-win situation of increasing oil production and reducing carbon emissions. Sinopec, PetroChina and CNOOC have successively launched million-ton CCUS demonstration projects. Sinopec Qilu-Shengli million-ton CO2 oil recovery project has started field implementation in March 2022. Compared with other EOR technologies, CO2 injection and EOR technology has many advantages. For example, CO2 has the characteristics of strong injection capacity, extraction of light components, reduction of crude oil viscosity, expansion of crude oil volume and improvement of crude oil flow capacity. In particular, when the formation pressure is higher than the minimum miscible pressure (MMP-Minimum Miscible Pressure), miscibility is achieved through multiple contacts, the interfacial tension between the displacement fluid and the displaced fluid is completely eliminated, the capillary number becomes infinite, the residual oil saturation can be reduced to the minimum value, and the displacement efficiency is close to 100%.
[0003] The miscibility of CO2 flooding is affected by reservoir temperature, reservoir pressure level, crude oil composition and injected gas composition, and the miscibility can be improved through the following two ways: one is to increase the reservoir pressure to above MMP; the other is to reduce MMP by changing the injected gas composition, such as adding chemicals or other gas components. The cost of injecting miscibility reducers is high, and mines have not yet successfully applied them. The current method of increasing reservoir pressure generally refers to increasing the formation pressure before gas injection, such as the advanced gas injection method. This method cannot guarantee the pressure level between the injection and production wells during the gas injection process, and it is difficult to guarantee the miscibility during the entire gas injection process. Summary of the invention
[0004] The purpose of the embodiments of the present invention is to provide a method, system and device for optimizing the miscibility of carbon dioxide flooding to solve the above technical problems.
[0005] In order to achieve the above object, an embodiment of the present invention provides a method for optimizing the miscibility of carbon dioxide flooding, the method comprising:
[0006] Obtain injection and production parameters and well pattern parameters before and after CO2 flooding;
[0007] Based on the injection-production parameters and the well pattern parameters, the phase state parameters before and after carbon dioxide flooding are determined using a preset proxy model; wherein the preset proxy model includes the corresponding relationship between the injection-production parameters and the well pattern parameters and the phase state parameters before and after carbon dioxide flooding, respectively, and the phase state parameters include miscibility parameters and miscibility driving state parameters;
[0008] According to the phase parameters and the preset proxy model, a control optimization strategy is determined using a preset control optimization model;
[0009] Based on the control optimization strategy, the injection and production parameters and the well network parameters are adjusted.
[0010] Optionally, the phase state parameters include miscibility degree parameters and miscibility driving state parameters; the preset proxy model includes a first preset proxy model and a second preset proxy model;
[0011] The phase parameters before and after carbon dioxide flooding are determined based on the injection and production parameters and the well pattern parameters using a preset proxy model, including:
[0012] The injection-production parameters and the well pattern parameters are input into a first preset proxy model to determine a miscibility parameter; wherein the first preset proxy model includes a correspondence between the miscibility parameter and the injection-production parameters and a correspondence between the miscibility parameter and the well pattern parameter.
[0013] Optionally, the determining of the phase parameters before and after carbon dioxide flooding based on the injection and production parameters and the well pattern parameters by using a preset proxy model also includes:
[0014] The injection-production parameters and the well pattern parameters are input into a second preset proxy model to determine the miscible driving state parameters; wherein the second preset proxy model includes the corresponding relationship between the miscible driving state parameters and the injection-production parameters and the corresponding relationship between the miscible driving state parameters and the well pattern parameters.
[0015] Optionally, determining a control optimization strategy based on the phase parameters and the preset proxy model using a preset control optimization model includes:
[0016] Calculating the current miscibility of carbon dioxide flooding according to the miscibility parameter and the miscibility driving state parameter;
[0017] Determining whether the current miscibility level is lower than a preset miscibility level;
[0018] If yes, then according to the phase parameters and the preset proxy model, a preset control optimization model is used to determine a control optimization strategy;
[0019] If not, there is no need to optimize the miscibility.
[0020] Optionally, determining a control optimization strategy based on the phase state parameters using a preset control optimization model includes:
[0021] Comparing the phase state parameters with the preset phase state conditions in the preset control optimization model to determine the phase state parameters that do not meet the preset phase state conditions;
[0022] Based on the preset phase conditions and the phase parameters that do not meet the preset phase conditions, a control optimization strategy is determined using the first preset proxy model and the second preset proxy model.
[0023] Optionally, the determining of the control optimization strategy based on the preset phase condition and the phase parameter that does not meet the preset phase condition by using the first preset proxy model and the second preset proxy model includes:
[0024] Based on the preset phase state conditions and the phase state parameters that do not meet the preset phase state conditions, a phase state parameter adjustment amount is determined; wherein the phase state parameter adjustment amount includes a miscibility degree parameter adjustment amount and a miscibility driving state parameter adjustment amount;
[0025] Based on the phase parameter adjustment amount, the first preset proxy model and the second preset proxy model, a control optimization strategy is determined using a preset intelligent optimization algorithm.
[0026] Optionally, based on the phase parameter adjustment amount, the first preset proxy model and the second preset proxy model, a control optimization strategy is determined using a preset intelligent optimization algorithm, including:
[0027] Based on the phase parameter adjustment amount, determining the injection and production parameters and well pattern parameters corresponding to the phase parameters that do not meet the preset phase conditions from the first preset proxy model and the second preset proxy model;
[0028] Based on the phase parameter adjustment amount and the injection and production parameters and well network parameters corresponding to the phase parameters that do not meet the preset phase conditions, a control optimization strategy is determined using an intelligent optimization algorithm in a preset control optimization model.
[0029] In a second aspect of an embodiment of the present invention, a system for optimizing the miscibility of carbon dioxide flooding is provided, the system comprising:
[0030] Data acquisition module, used to obtain injection and production parameters and well pattern parameters before and after carbon dioxide flooding;
[0031] A phase state determination module, for determining the phase state parameters before and after carbon dioxide flooding based on the injection and production parameters and the well pattern parameters using a preset proxy model; wherein the preset proxy model includes the corresponding relationship between the injection and production parameters and the well pattern parameters and the phase state parameters before and after carbon dioxide flooding, respectively, and the phase state parameters include miscibility parameters and miscibility driving state parameters;
[0032] A strategy generation module, used to determine a control optimization strategy based on the phase parameters and the preset proxy model using a preset control optimization model;
[0033] The parameter optimization module is used to adjust the injection and production parameters and the well network parameters based on the control optimization strategy.
[0034] A third aspect of the present application provides an electronic device configured to execute the above-mentioned method for optimizing the miscibility of carbon dioxide flooding.
[0035] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the above-mentioned method for optimizing the miscibility of carbon dioxide flooding.
[0036] The embodiment of the present invention obtains the injection and production parameters and well network parameters before and after carbon dioxide flooding, and based on the injection and production parameters and well network parameters before and after carbon dioxide flooding, uses a preset proxy model to determine the phase parameters before and after carbon dioxide flooding, and uses a preset control optimization model to determine the control optimization strategy according to the phase parameters and the preset proxy model, and adjusts the injection and production parameters and well network parameters based on the control optimization strategy. That is, the embodiment of the present invention uses a preset control optimization model to optimize the miscibility of carbon dioxide flooding, improves the reliability and accuracy of optimization, and provides a scientific basis for improving the CO2 flooding effect in continental oil reservoirs.
[0037] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:
[0039] Figure 1 The embodiment of the present invention provides a flow chart of a method for optimizing the miscibility of carbon dioxide flooding;
[0040] Figure 2 The embodiment of the present invention provides a functional module schematic diagram of a system for optimizing the miscibility of carbon dioxide in oil recovery. DETAILED DESCRIPTION
[0041] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.
[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.
[0043] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.
[0044] Embodiment 1
[0045] Please refer to Figure 1 , Figure 1 It is a flow chart of a method for optimizing the miscibility of carbon dioxide flooding provided in this embodiment.
[0046] Step S100: Obtaining injection and production parameters and well pattern parameters before and after carbon dioxide flooding.
[0047] The injection-production parameters may include parameters such as injection multiples and injection-production methods, and the well pattern parameters may be the well pattern spacing.
[0048] It can be understood that in this step, the dynamic and static data of the reservoir in the target work area are obtained, and based on the dynamic and static data of the reservoir, a three-dimensional geological model is established using Petrel software, and the three-dimensional geological model is used to simulate the injection and production parameters and well network parameters derived after fitting the fluid phase before and after CO2 injection.
[0049] The dynamic and static data of oil reservoirs include static geological parameters of oil reservoirs and dynamic production parameters. The static geological parameters of oil reservoirs may include porosity, permeability, effective thickness, flow barrier, distribution of reservoir and non-reservoir interface, etc.; the dynamic production parameters may include oil well production parameters, pumping well production parameters, etc.
[0050] Step S200: Based on the injection-production parameters and the well network parameters, the phase state parameters before and after the carbon dioxide flooding are determined using a preset proxy model; wherein the preset proxy model includes the corresponding relationship between the injection-production parameters and the well network parameters and the phase state parameters before and after the carbon dioxide flooding, respectively, and the phase state parameters include miscibility parameters and miscibility driving state parameters.
[0051] Phase state parameters include miscibility parameters and miscibility driving state parameters. Miscibility parameters may include miscibility volume coefficient, near-miscibility volume coefficient and miscibility; miscibility driving state parameters may include gas-oil ratio, water content and oil increase, etc.
[0052] The preset proxy model includes a first preset proxy model and a second preset proxy model. The first preset proxy model includes the correspondence between the miscibility degree parameter and the injection and production parameter and the correspondence between the miscibility degree parameter and the well network parameter. The second preset proxy model includes the correspondence between the miscibility driving state parameter and the injection and production parameter and the correspondence between the miscibility driving state parameter and the well network parameter.
[0053] In this step, the injection-production parameters and the well pattern parameters are specifically input into the first preset proxy model to determine the miscibility degree parameters, and the injection-production parameters and the well pattern parameters are specifically input into the second preset proxy model to determine the miscibility driving state parameters.
[0054] Step S300: According to the phase parameters and the preset proxy model, a control optimization strategy is determined using a preset control optimization model.
[0055] This step specifically includes: calculating the current miscibility of carbon dioxide flooding according to the miscibility parameter and the miscibility driving state parameter, judging whether the current miscibility is lower than the preset miscibility, if not, there is no need to optimize the miscibility; if so, comparing the phase parameters with the preset phase conditions in the preset control optimization model, determining the phase parameters that do not meet the preset phase conditions, determining the phase parameter adjustment amount based on the preset phase conditions and the phase parameters that do not meet the preset phase conditions, wherein the phase parameter adjustment amount includes the miscibility parameter adjustment amount and the miscibility driving state parameter adjustment amount, further based on the phase parameter adjustment amount, determining the injection and production parameters and well network parameters corresponding to the phase parameters that do not meet the preset phase conditions from the first preset proxy model and the second preset proxy model, and determining the control optimization strategy based on the phase parameter adjustment amount and the injection and production parameters and well network parameters corresponding to the phase parameters that do not meet the preset phase conditions using the intelligent optimization algorithm in the preset control optimization model.
[0056] That is, this step uses the preset phase conditions in the preset control optimization model to determine the phase parameters that do not meet the preset phase conditions, and then randomly searches the injection and production parameters and well network parameters corresponding to the phase parameters that do not meet the preset phase conditions from the preset proxy model, generates a corresponding control optimization strategy, and adjusts the injection and production parameters and well network parameters corresponding to the phase parameters that do not meet the preset phase conditions according to the control optimization strategy, so that the phase parameters all meet the preset phase conditions, indirectly optimizing the miscibility and improving the optimization reliability.
[0057] The preset phase conditions in the preset control optimization model may include: the miscibility degree of the entire CO2 injection and production area is greater than 70%; the CO2 molar content in the circulating injection gas is greater than 90%.
[0058] The optimization strategies corresponding to the intelligent optimization algorithm in the preset control optimization model may include: taking the injection-production method and injection-production system as decision variables; taking the maximum oil increase and carbon storage as the goal; keeping the bottom hole pressure and injection-production speed of the injection-production well within the preset range; the injection method can be adjusted to one or a combination of continuous gas injection, water-gas alternation, intermittent gas injection, and flooding and spitting; and the highest production gas-oil ratio exists in the entire area and in a single well.
[0059] Step S400: Based on the control optimization strategy, the injection and production parameters and the well pattern parameters are adjusted.
[0060] The embodiment of the present invention obtains the injection and production parameters and well network parameters before and after carbon dioxide flooding, and based on the injection and production parameters and well network parameters before and after carbon dioxide flooding, uses a preset proxy model to determine the phase parameters before and after carbon dioxide flooding, and uses a preset control optimization model to determine the control optimization strategy according to the phase parameters and the preset proxy model, and adjusts the injection and production parameters and well network parameters based on the control optimization strategy. That is, the embodiment of the present invention uses a preset control optimization model to optimize the miscibility of carbon dioxide flooding, improves the reliability and accuracy of optimization, and provides a scientific basis for improving the CO2 flooding effect in continental oil reservoirs.
[0061] Embodiment 2
[0062] Please refer to Figure 2 , Figure 2 Schematic diagram of functional modules of a system 200 for optimizing the miscibility of carbon dioxide flooding provided in an embodiment of the present application.
[0063] The data acquisition module 210 is used to obtain the injection and production parameters and well pattern parameters before and after the carbon dioxide flooding;
[0064] The phase state determination module 220 is used to determine the phase state parameters before and after carbon dioxide flooding based on the injection and production parameters and the well pattern parameters using a preset proxy model; wherein the preset proxy model includes the corresponding relationship between the injection and production parameters and the well pattern parameters and the phase state parameters before and after carbon dioxide flooding, respectively, and the phase state parameters include miscibility parameters and miscibility driving state parameters;
[0065] A strategy generation module 230 is used to determine a control optimization strategy based on the phase parameters and the preset proxy model using a preset control optimization model;
[0066] The parameter optimization module 240 is used to adjust the injection and production parameters and the well pattern parameters based on the control optimization strategy.
[0067] It should be understood that the device corresponds to the above-mentioned method embodiment for optimizing the miscibility of carbon dioxide flooding, and can perform the various steps involved in the above-mentioned method embodiment. The specific functions of the device can be found in the above description, and the detailed description is appropriately omitted here to avoid repetition. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.
[0068] Embodiment 3
[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0070] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0071] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0072] Embodiment 4
[0073] An embodiment of the present invention further provides a computer-readable storage medium having instructions stored thereon, which are used to execute a program having method steps for optimizing the miscibility of carbon dioxide flooding when executed by a processor.
[0074] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
[0075] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0078] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.
[0079] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.
[0080] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0081] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A method for optimizing the miscibility of carbon dioxide flooding, characterized in that: The method comprises: Obtain injection and production parameters and well pattern parameters before and after CO2 flooding; Based on the injection-production parameters and the well pattern parameters, the phase state parameters before and after carbon dioxide flooding are determined using a preset proxy model; wherein the preset proxy model includes the corresponding relationship between the injection-production parameters and the well pattern parameters and the phase state parameters before and after carbon dioxide flooding, respectively, and the phase state parameters include miscibility parameters and miscibility driving state parameters; According to the phase parameters and the preset proxy model, a control optimization strategy is determined using a preset control optimization model; Based on the control optimization strategy, the injection and production parameters and the well network parameters are adjusted.
2. The method for optimizing the miscibility of carbon dioxide flooding according to claim 1, characterized in that: The preset proxy model includes a first preset proxy model and a second preset proxy model; The phase parameters before and after carbon dioxide flooding are determined based on the injection and production parameters and the well pattern parameters using a preset proxy model, including: The injection-production parameters and the well pattern parameters are input into a first preset proxy model to determine a miscibility parameter; wherein the first preset proxy model includes a correspondence between the miscibility parameter and the injection-production parameters and a correspondence between the miscibility parameter and the well pattern parameter.
3. The method for optimizing the miscibility of carbon dioxide flooding according to claim 2, characterized in that: The method of determining the phase parameters before and after carbon dioxide flooding based on the injection and production parameters and the well pattern parameters by using a preset proxy model also includes: The injection-production parameters and the well pattern parameters are input into a second preset proxy model to determine the miscible driving state parameters; wherein the second preset proxy model includes the corresponding relationship between the miscible driving state parameters and the injection-production parameters and the corresponding relationship between the miscible driving state parameters and the well pattern parameters.
4. The method for optimizing the miscibility of carbon dioxide flooding according to claim 3, characterized in that: The step of determining a control optimization strategy based on the phase parameters and the preset proxy model using a preset control optimization model includes: Calculating the current miscibility of carbon dioxide flooding according to the miscibility parameter and the miscibility driving state parameter; Determining whether the current miscibility level is lower than a preset miscibility level; If yes, then according to the phase parameters and the preset proxy model, a preset control optimization model is used to determine a control optimization strategy; If not, there is no need to optimize the miscibility.
5. The method for optimizing the miscibility of carbon dioxide flooding according to claim 4, characterized in that: According to the phase parameters and the preset proxy model, a control optimization strategy is determined using a preset control optimization model, including: Comparing the phase state parameters with the preset phase state conditions in the preset control optimization model to determine the phase state parameters that do not meet the preset phase state conditions; Based on the preset phase conditions and the phase parameters that do not meet the preset phase conditions, a control optimization strategy is determined using the first preset proxy model and the second preset proxy model.
6. The method for optimizing the miscibility of carbon dioxide flooding according to claim 5, characterized in that: The step of determining a control optimization strategy based on the preset phase condition and the phase parameters that do not meet the preset phase condition by using the first preset proxy model and the second preset proxy model includes: Based on the preset phase state conditions and the phase state parameters that do not meet the preset phase state conditions, a phase state parameter adjustment amount is determined; wherein the phase state parameter adjustment amount includes a miscibility degree parameter adjustment amount and a miscibility driving state parameter adjustment amount; Based on the phase parameter adjustment amount, the first preset proxy model and the second preset proxy model, a control optimization strategy is determined using a preset intelligent optimization algorithm.
7. The method for optimizing the miscibility of carbon dioxide flooding according to claim 6, characterized in that: Based on the phase parameter adjustment amount, the first preset proxy model and the second preset proxy model, a control optimization strategy is determined using a preset intelligent optimization algorithm, including: Based on the phase parameter adjustment amount, determining the injection and production parameters and well pattern parameters corresponding to the phase parameters that do not meet the preset phase conditions from the first preset proxy model and the second preset proxy model; Based on the phase parameter adjustment amount and the injection and production parameters and well network parameters corresponding to the phase parameters that do not meet the preset phase conditions, a control optimization strategy is determined using an intelligent optimization algorithm in a preset control optimization model.
8. A system for optimizing the miscibility of carbon dioxide flooding, characterized in that: The system comprises: Data acquisition module, used to obtain injection and production parameters and well pattern parameters before and after carbon dioxide flooding; A phase state determination module, for determining the phase state parameters before and after carbon dioxide flooding based on the injection and production parameters and the well pattern parameters using a preset proxy model; wherein the preset proxy model includes the corresponding relationship between the injection and production parameters and the well pattern parameters and the phase state parameters before and after carbon dioxide flooding, respectively, and the phase state parameters include miscibility parameters and miscibility driving state parameters; A strategy generation module, used to determine a control optimization strategy based on the phase parameters and the preset proxy model using a preset control optimization model; The parameter optimization module is used to adjust the injection and production parameters and the well network parameters based on the control optimization strategy.
9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method for optimizing the miscibility of carbon dioxide flooding according to any one of claims 1 to 7 is performed.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions for causing a machine to execute the method for optimizing the miscibility of carbon dioxide flooding according to any one of claims 1 to 7.