Method and apparatus for modeling stress in a carbon dioxide storage near wellbore region

Abstract

The application provides a carbon dioxide storage near-well area stress simulation method, device, equipment and storage medium. The method comprises the following steps: obtaining geological, engineering, rock mass physical property, fluid physical property and field monitoring data of a carbon dioxide target storage area near-well area as basic data; constructing a three-dimensional finite element model of the near-well area based on the basic data; performing spatial discretization on the three-dimensional finite element model based on the basic data and a preset boundary condition, and performing time domain discretization according to a construction period; building and training a stress field prediction model according to the spatial discretization result and the time domain discretization result; performing mixed solving of the near-well area stress field based on the stress field prediction model to obtain stress simulation data; and performing wellbore and casing stability evaluation and risk identification based on the stress simulation data. The technical scheme can provide reliable data support for safe operation of the carbon dioxide safe storage project.

Description

Technical Field

[0001] This application relates to the field of carbon dioxide geological storage technology, and in particular to a method, apparatus, equipment and storage medium for simulating near-wellbore stress in carbon dioxide storage. Background Technology

[0002] In related technologies, the near-wellbore area, as the core region for carbon dioxide injection, pressure disturbance, and stress concentration during carbon dioxide geological storage, is a key target area for engineering safety control. During supercritical high-pressure injection of carbon dioxide into the formation, it can trigger a sudden increase in pore pressure, formation temperature fluctuations, and drastic changes in the fluid seepage field, directly disrupting the original stress balance of the reservoir. This can easily induce stress redistribution in the surrounding rock, wellbore instability, casing deformation, fracture initiation and propagation, and even safety accidents such as sealing failure and carbon dioxide leakage. Early simulation can effectively prevent safety accidents; however, existing near-wellbore stress simulation technologies are limited to static three-dimensional spatial analysis, ignoring the dynamic evolution characteristics of stress over injection and storage time, and cannot reproduce the spatiotemporal variation patterns of stress under real engineering conditions. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Firstly, this application proposes a method for simulating stress in the near-wellbore area of ​​carbon dioxide storage. The method includes: acquiring geological, engineering, rock mass properties, fluid properties, and field monitoring data of the near-wellbore area of ​​the target carbon dioxide storage region as basic data; constructing a three-dimensional finite element model of the near-wellbore area based on the basic data; spatially discretizing the three-dimensional finite element model based on the basic data and preset boundary conditions, and then performing time-domain discretization according to the construction cycle; building and training a stress field prediction model based on the spatial and time-domain discretization results; performing a hybrid solution of the stress field in the near-wellbore area based on the stress field prediction model to obtain stress simulation data; and evaluating the stability of the wellbore and casing and identifying risks based on the stress simulation data.

[0005] In one implementation, the hydrodynamic multi-field coupled boundary conditions include mechanical boundaries, seepage boundaries, and temperature boundaries.

[0006] In one alternative implementation, the mechanical boundary includes at least one of the following: far-field in-situ stress boundary, wellbore pressure boundary, and formation bottom fixed constraint; the seepage boundary includes at least one of the following: constant flow injection boundary or constant pressure injection boundary, and formation far-field closed boundary; the temperature boundary includes at least one of the following: injected fluid temperature boundary and formation original temperature boundary.

[0007] In one implementation, the spatial discretization of the three-dimensional finite element model includes: performing spatial discretization on the three-dimensional finite element model based on the basic data and preset boundary conditions to obtain a finite element computational mesh.

[0008] In one implementation, the time-domain discretization according to the construction cycle includes: performing time-domain discretization based on a first time step in response to the construction cycle being a carbon dioxide injection period; and performing time-domain discretization based on a second time step in response to the construction cycle being a carbon dioxide storage period; wherein the second time step is shorter than the first time step.

[0009] In one implementation, the hybrid solution of the near-wellbore stress field includes: solving the initial stress field and the first stress field at the critical time step using the finite element method; and solving the second stress field at the ordinary time step based on the stress field prediction model.

[0010] Secondly, this application proposes a near-wellbore stress simulation device for carbon dioxide storage. The device includes: an acquisition module for acquiring geological, engineering, rock mass properties, fluid properties, and field monitoring data of the near-wellbore area of ​​the target carbon dioxide storage region as basic data; a first processing module for constructing a three-dimensional finite element model of the near-wellbore area based on the basic data; a second processing module for spatially discretizing the three-dimensional finite element model based on the basic data and preset boundary conditions, and performing time-domain discretization according to the construction cycle; a third processing module for building and training a stress field prediction model based on the spatial and time-domain discretization results; a fourth processing module for performing a hybrid solution of the near-wellbore stress field based on the stress field prediction model to obtain stress simulation data; and a fifth processing module for evaluating the stability of the wellbore and casing and identifying risks based on the stress simulation data. In one implementation, the hydrodynamic multi-field coupled boundary conditions include mechanical boundaries, seepage boundaries, and temperature boundaries.

[0011] In one alternative implementation, the mechanical boundary includes at least one of the following: far-field in-situ stress boundary, wellbore pressure boundary, and formation bottom fixed constraint; the seepage boundary includes at least one of the following: constant flow injection boundary or constant pressure injection boundary, and formation far-field closed boundary; the temperature boundary includes at least one of the following: injected fluid temperature boundary and formation original temperature boundary.

[0012] In one implementation, the second processing module can be used to: spatially discretize the three-dimensional finite element model based on the basic data and preset boundary conditions to obtain a finite element computational mesh.

[0013] In one implementation, the second processing module can be used to: perform time-domain discretization based on a first time step in response to the construction period being a carbon dioxide injection period; and perform time-domain discretization based on a second time step in response to the construction period being a carbon dioxide storage period; wherein the second time step is shorter than the first time step.

[0014] In one implementation, the fourth processing module can be used to: solve the initial stress field and the first stress field of the key time step using the finite element method; and solve the second stress field of the ordinary time step based on the stress field prediction model.

[0015] Thirdly, this application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0016] Fourthly, this application proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect.

[0017] Fifthly, this application proposes a program product comprising at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method described in the first aspect.

[0018] The carbon dioxide storage near-wellbore stress simulation method, apparatus, equipment, and storage medium provided in this application are based on multi-source measured geological, engineering, rock mass and fluid properties, and monitoring data of the near-wellbore area of ​​the carbon dioxide storage region. They sequentially complete three-dimensional finite element geometric modeling, spatial and temporal dual discretization processing, construct and train a stress field prediction model, and accurately obtain near-wellbore stress field evolution data using a hybrid solution method of finite element + prediction model. This allows for the analysis of wellbore and casing structure stability and safety risk identification. It can efficiently and accurately characterize the dynamic changes in stress in the near-wellbore area of ​​carbon dioxide storage, providing reliable data support for the safe operation of carbon dioxide safe storage projects.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1This is a schematic flowchart of a near-wellbore stress simulation method for carbon dioxide storage provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a near-wellbore stress simulation device for carbon dioxide storage provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] The following describes a method and apparatus for simulating near-wellbore stress in carbon dioxide storage, according to embodiments of this application, with reference to the accompanying drawings.

[0024] Figure 1 This is a schematic flowchart of a near-wellbore stress simulation method for carbon dioxide storage provided in an embodiment of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps: S1. Obtain geological, engineering, rock mass properties, fluid properties, and field monitoring data of the near-well area of ​​the carbon dioxide target storage area as basic data.

[0025] For example, geological, engineering, rock mass properties, fluid properties, and field monitoring data of the reservoir area within a preset range around the carbon dioxide injection wellbore are obtained as basic data.

[0026] S2. Based on the basic data, construct a three-dimensional finite element model of the near-wellbore area.

[0027] In one implementation, the three-dimensional finite element model includes the well casing, cement sheath, near-well rock, and far-well rock.

[0028] In one alternative implementation, the hydrodynamic multi-field coupled boundary conditions include mechanical boundaries, seepage boundaries, and temperature boundaries; the mechanical boundaries include at least one of the following: far-field in-situ stress boundary, wellbore pressure boundary, and formation bottom fixed constraint; the seepage boundaries include at least one of the following: constant flow injection boundary, constant pressure injection boundary, and formation far-field closed boundary; the temperature boundaries include at least one of the following: injected fluid temperature boundary and formation original temperature boundary.

[0029] For example, with the injection wellbore as the center, the radial range is 5m-20m (as the near-wellbore disturbance zone), and the longitudinal range covers the reservoir section and the caprock section; three-dimensional models corresponding to the wellbore casing, cement sheath, near-wellbore surrounding rock, and far-wellbore surrounding rock are established in sequence; a hybrid structured and unstructured mesh is used to divide the three-dimensional model into meshes, and a three-dimensional finite element model of the near-wellbore area is constructed.

[0030] S3. Based on the basic data and preset boundary conditions, the three-dimensional finite element model is spatially discretized and then discretized in the time domain according to the construction cycle.

[0031] For example, by combining basic data and preset boundary conditions, the constructed three-dimensional finite element geometric model is meshed to complete spatial discretization to obtain the finite element computational mesh. At the same time, the two major construction cycles of carbon dioxide injection and storage are distinguished, and the corresponding time steps are matched to perform differentiated time-domain discretization.

[0032] In one implementation, the aforementioned hydrodynamic multi-field coupled boundary conditions include mechanical boundaries, seepage boundaries, and temperature boundaries. The mechanical boundaries include at least one of the following: far-field in-situ stress boundary, wellbore pressure boundary, and formation bottom fixed constraint; the seepage boundaries include at least one of the following: constant flow injection boundary or constant pressure injection boundary, and formation far-field closed boundary; the temperature boundaries include at least one of the following: injected fluid temperature boundary and original formation temperature boundary.

[0033] S4. Based on the spatial discretization results and the temporal discretization results, build and train the stress field prediction model.

[0034] For example, the finite element mesh model data obtained by spatial discretization and the time-domain time step data after differentiated discretization under different construction cycles are used as model input samples. A stress field prediction model is built by combining the geomechanical variation law of the near-wellbore area. Based on the sample data, the model is iteratively trained, the parameters are optimized and the accuracy is verified to obtain the stress field prediction model. In one implementation, the stress field prediction model is constructed based on a long short-term memory network and a multilayer perceptron. Its input features are injection pressure, injection rate, formation temperature, rock mass parameters, and time step, and its output labels are element (polyhedral finite element) stress, displacement, pore pressure, and temperature. The long short-term memory network is used to extract time series features and learn the stress time series evolution law. The multilayer perceptron is used to fit the nonlinear mapping relationship between spatial parameters and stress.

[0035] S5. Based on the stress field prediction model, perform a hybrid solution of the stress field in the near-wellbore area to obtain stress simulation data.

[0036] In one implementation, the above-mentioned hybrid solution of the near-wellbore stress field includes: using the finite element method to solve the stress field at the initial moment, then using the finite element method to solve the key time step that meets the preset conditions, and using the stress field prediction model to solve the ordinary time step that meets the preset conditions.

[0037] For example, a hybrid solution method combining finite element method and model prediction is adopted. The finite element method is used to accurately solve the stress field at the initial moment of the model and the first stress field corresponding to the key time step that meets the preset conditions. At the same time, the trained stress field prediction model is used to quickly calculate the second stress field of ordinary time steps. The stress results of all time steps are integrated to obtain complete and continuous near-wellbore stress simulation data. Therefore, by periodically calibrating the finite element results, the accumulated errors in the stress field prediction model can be eliminated, achieving a balance between computational efficiency and simulation accuracy. S6. Based on stress simulation data, conduct wellbore and casing stability evaluation and risk identification.

[0038] For example, stress evolution patterns are determined based on stress simulation data, and optimal injection pressure and rate are identified through sensitivity analysis to avoid stress overload while optimizing carbon dioxide injection parameters. For stress concentration areas, control measures such as casing reinforcement, cementing, and controlled injection are proposed. A long-term stress evolution prediction model is established to provide early warning of hysteretic instability risks during the storage period. This enables intelligent optimization of injection parameters, wellbore and casing stability evaluation, safety control plan development, and long-term risk early warning, facilitating the transformation from simulation analysis to engineering implementation and providing direct technical support for the design, construction, and long-term operation and maintenance of carbon dioxide storage projects.

[0039] By implementing the embodiments of this application, based on multi-source measured geological, engineering, rock mass and fluid properties and monitoring data of the near-wellbore area of ​​the carbon dioxide storage region, three-dimensional finite element geometric modeling and spatial and temporal dual discretization processing are sequentially completed to construct and train a stress field prediction model. A hybrid solution method of finite element + prediction model is used to accurately obtain the stress field evolution data of the near-wellbore area, thereby conducting wellbore and casing structure stability analysis and safety risk identification. This can efficiently and accurately characterize the dynamic change law of stress in the near-wellbore area of ​​carbon dioxide storage, providing reliable data support for the safe operation of carbon dioxide safe storage projects.

[0040] Please see Figure 2 , Figure 2 This is a schematic diagram of a near-wellbore stress simulation device for carbon dioxide storage provided in an embodiment of this application. Figure 2As shown, the device 200 includes: an acquisition module 201, used to acquire geological, engineering, rock mass properties, fluid properties, and field monitoring data of the near-wellbore area of ​​the carbon dioxide target storage area as basic data; a first processing module 202, used to construct a three-dimensional finite element model of the near-wellbore area based on the basic data; a second processing module 203, used to spatially discretize the three-dimensional finite element model based on the basic data and preset boundary conditions, and to perform time-domain discretization according to the construction cycle; a third processing module 204, used to build and train a stress field prediction model based on the spatial and time-domain discretization results; a fourth processing module 205, used to perform a hybrid solution of the stress field in the near-wellbore area based on the stress field prediction model to obtain stress simulation data; and a fifth processing module 206, used to evaluate the stability of the wellbore and casing and identify risks based on the stress simulation data.

[0041] In one implementation, the multi-field coupled boundary conditions of hydrodynamics include mechanical boundaries, seepage boundaries, and temperature boundaries.

[0042] In one alternative implementation, the mechanical boundary includes at least one of the following: far-field in-situ stress boundary, wellbore pressure boundary, and formation bottom fixed constraint; the seepage boundary includes at least one of the following: constant flow injection boundary or constant pressure injection boundary, and formation far-field closed boundary; the temperature boundary includes at least one of the following: injected fluid temperature boundary, and formation original temperature boundary.

[0043] In one implementation, the second processing module 203 can be used to: spatially discretize the three-dimensional finite element model based on basic data and preset boundary conditions to obtain a finite element computational mesh.

[0044] In one implementation, the second processing module 203 can be used to: perform time-domain discretization based on a first time step in response to the construction period being a carbon dioxide injection period; and perform time-domain discretization based on a second time step in response to the construction period being a carbon dioxide storage period; wherein the second time step is shorter than the first time step.

[0045] In one implementation, the fourth processing module 205 can be used to: solve the initial stress field and the first stress field of the key time step using the finite element method; and solve the second stress field of the ordinary time step based on the stress field prediction model.

[0046] The apparatus described in this application can be based on multi-source measured geological, engineering, rock mass, and fluid properties and monitoring data of the near-wellbore area of ​​the carbon dioxide storage region. It sequentially completes three-dimensional finite element geometric modeling, spatial and temporal dual discretization processing, constructs and trains a stress field prediction model, and accurately obtains near-wellbore stress field evolution data using a hybrid solution method of finite element + prediction model. This allows for the analysis of wellbore and casing structure stability and safety risk identification. It can efficiently and accurately characterize the dynamic changes in stress in the near-wellbore area of ​​carbon dioxide storage, providing reliable data support for the safe operation of carbon dioxide safe storage projects.

[0047] It should be noted that the foregoing explanation of the near-wellbore stress simulation method for carbon dioxide storage also applies to the near-wellbore stress simulation device for carbon dioxide storage in this embodiment, and will not be repeated here.

[0048] To implement the above embodiments, this application also proposes an electronic device. Please see [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 300 includes: a processor 301, and a memory 302 communicatively connected to the processor 301; the memory 302 stores computer execution instructions; the processor 301 executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0049] To implement the above embodiments, this application also proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods provided in the foregoing embodiments.

[0050] To implement the above embodiments, this application also proposes a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method provided in the foregoing embodiments.

[0051] It should be noted that the acquisition, transmission, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0052] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0053] It is worth noting that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0054] In the description of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0055] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0056] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0057] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0058] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0059] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0060] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0061] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0062] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for simulating near-wellbore stress in carbon dioxide storage, characterized in that, include: Geological, engineering, rock mass properties, fluid properties, and field monitoring data of the near-well area of ​​the carbon dioxide target storage area were obtained as basic data; Based on the aforementioned basic data, a three-dimensional finite element model of the near-wellbore area is constructed. Based on the aforementioned basic data and preset boundary conditions, the three-dimensional finite element model is spatially discretized and then time-domain discretized according to the construction cycle. Based on the spatial and temporal discretization results, a stress field prediction model was built and trained. Based on the stress field prediction model, a hybrid solution for the stress field in the near-wellbore area is performed to obtain stress simulation data; Based on the stress simulation data, the stability of the wellbore and casing is evaluated and risks are identified.

2. The method according to claim 1, characterized in that, The hydrodynamic multi-field coupled boundary conditions include mechanical boundaries, seepage boundaries, and temperature boundaries.

3. The method according to claim 2, characterized in that, The mechanical boundary includes at least one of the following: far-field in-situ stress boundary, wellbore internal pressure boundary, and formation bottom fixed constraint; The seepage boundary includes at least one of the following: constant flow injection boundary or constant pressure injection boundary, formation far-field closure boundary. The temperature boundary includes at least one of the following: the injected fluid temperature boundary, and the original formation temperature boundary.

4. The method according to claim 1, characterized in that, The spatial discretization of the three-dimensional finite element model includes: Based on the aforementioned basic data and preset boundary conditions, the three-dimensional finite element model is spatially discretized to obtain a finite element computational mesh.

5. The method according to claim 1, characterized in that, The time-domain discretization according to the construction cycle includes: In response to the construction period being a carbon dioxide injection period, time-domain discretization is performed based on the first time step; In response to the construction period being a carbon dioxide storage period, time-domain discretization is performed based on a second time step; wherein the second time step is shorter than the first time step.

6. The method according to claim 1, characterized in that, The process of performing a hybrid solution for the near-wellbore stress field includes: The initial stress field and the first stress field at the critical time step were solved using the finite element method. The second stress field at the ordinary time step is solved based on the stress field prediction model.

7. A near-wellbore stress simulation device for carbon dioxide storage, characterized in that, include: The acquisition module is used to acquire geological, engineering, rock mass properties, fluid properties, and field monitoring data of the near-well area of ​​the carbon dioxide target storage area as basic data; The first processing module is used to construct a three-dimensional finite element model of the near-wellbore area based on the aforementioned basic data; The second processing module is used to spatially discretize the three-dimensional finite element model based on the basic data and preset boundary conditions, and to perform time-domain discretization according to the construction cycle. The third processing module is used to build and train the stress field prediction model based on the spatial discretization results and the temporal discretization results. The fourth processing module is used to perform a hybrid solution of the near-wellbore stress field based on the stress field prediction model to obtain stress simulation data. The fifth processing module is used to evaluate the stability of the wellbore and casing and identify risks based on the stress simulation data.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A storage medium storing instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method of any one of claims 1 to 6.

10. A program product comprising at least one of a program and instructions, characterized in that, When at least one of the program or instructions is executed by an electronic device, it implements the steps of the method according to any one of claims 1 to 6.

Images