Method for warning formation pressure of carbon dioxide storage project and device thereof

Abstract

The application provides a stratum pressure early warning method and device for a carbon dioxide storage project, and the method comprises the following steps: obtaining pressure monitoring data of a ground, a wellbore and a stratum in a carbon dioxide geological storage area; obtaining stratum parameters and carbon dioxide injection parameters; preprocessing the pressure monitoring data to obtain standard pressure data; obtaining a stratum pressure prediction value through a pre-trained stratum pressure prediction model according to the current standard pressure data, the current stratum parameters and the current injection parameters; and performing graded early warning based on the stratum pressure prediction value. Through the technical scheme, the timeliness and accuracy of stratum pressure control in the storage area can be effectively improved.

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 early warning of formation pressure in carbon dioxide storage projects. Background Technology

[0002] High-pressure injection is a crucial component of carbon dioxide geological storage projects. During high-pressure injection, the distribution and evolution of formation pressure directly determine storage safety, injection efficiency, and long-term storage capacity: excessively low formation pressure leads to slow injection rates and insufficient reservoir utilization; excessively high pressure can exceed the pressure-bearing limits of caprocks and interlayers, inducing safety accidents such as rock fracture, fault activation, and carbon dioxide leakage; interlayer pressure imbalance can also exacerbate interlayer crosstalk and reduce the overall storage efficiency of multi-layer reservoirs. Current pressure control in carbon dioxide geological storage projects mostly relies on single-point monitoring, making it difficult to achieve real-time acquisition of pressure across layers and the entire area, resulting in monitoring blind spots; moreover, it largely depends on manual interpretation and simple numerical analysis, failing to quickly identify abnormal pressure changes, and data processing is lagging. Summary of the Invention

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

[0004] In a first aspect, this application proposes a formation pressure early warning method for carbon dioxide sequestration projects. The method includes: acquiring pressure monitoring data of the surface, wellbore, and formation in the carbon dioxide geological sequestration area; acquiring formation parameters and carbon dioxide injection parameters; preprocessing the pressure monitoring data to obtain standard pressure data; and obtaining formation pressure prediction values ​​based on the current standard pressure data, current formation parameters, and current injection parameters using a pre-trained formation pressure prediction model, and then issuing graded early warnings based on the formation pressure prediction values.

[0005] In one implementation, the pressure monitoring data includes at least one of the following: stratified reservoir pore pressure, interlayer pressure, caprock pressure, wellbore pressure, interlayer interface pressure, wellhead injection pressure, injection rate, pressure change rate, and cumulative injection volume; the formation parameters include at least one of the following: permeability, porosity, rock strength, geostress, and formation critical fracture pressure; the injection parameters include at least one of the following: injection pressure, injection rate, temperature, and injection time.

[0006] In one implementation, the formation pressure prediction model is constructed based on a convolutional neural network, a long short-term memory network, and an attention mechanism.

[0007] In one implementation, the step of performing graded early warning based on the formation pressure prediction value includes: obtaining graded early warning levels according to the formation pressure prediction value; wherein the graded early warning levels, from low to high, include blue early warning, yellow early warning, orange early warning, and red early warning; and performing stratified pressure regulation according to the graded early warning levels.

[0008] In one optional implementation, obtaining the graded early warning level based on the formation pressure prediction value includes: issuing a blue early warning when the formation pressure prediction value is less than a preset multiple of the early warning pressure; issuing a yellow early warning when the preset multiple of the early warning pressure is less than or equal to the formation pressure prediction value; issuing an orange early warning when the formation pressure prediction value is greater than or equal to the early warning pressure; and issuing a red early warning when the formation pressure prediction value is greater than or equal to the formation critical fracturing pressure.

[0009] In one alternative implementation, the formula for calculating the warning pressure is:

[0010]

[0011] In the formula, To provide early warning of pressure, For safety reasons, This is the critical fracture pressure of the formation.

[0012] Secondly, this application proposes a formation pressure early warning device for carbon dioxide sequestration projects. The device includes: a first acquisition module for acquiring pressure monitoring data of the surface, wellbore, and formation in the carbon dioxide geological sequestration area; a second acquisition module for acquiring formation parameters and carbon dioxide injection parameters; a first processing module for preprocessing the pressure monitoring data to acquire standard pressure data; a second processing module for acquiring predicted formation pressure values ​​based on the current standard pressure data, current formation parameters, and current injection parameters using a pre-trained formation pressure prediction model; and a third processing module for providing graded early warnings based on the predicted formation pressure values.

[0013] In one implementation, the pressure monitoring data includes at least one of the following: stratified reservoir pore pressure, interlayer pressure, caprock pressure, wellbore pressure, interlayer interface pressure, wellhead injection pressure, injection rate, pressure change rate, and cumulative injection volume; the formation parameters include at least one of the following: permeability, porosity, rock strength, geostress, and formation critical fracture pressure; the injection parameters include at least one of the following: injection pressure, injection rate, temperature, and injection time.

[0014] In one implementation, the formation pressure prediction model is constructed based on a convolutional neural network, a long short-term memory network, and an attention mechanism.

[0015] In one implementation, the third processing module can be used to: obtain a graded early warning level based on the predicted formation pressure value; wherein the graded early warning level includes blue early warning, yellow early warning, orange early warning and red early warning in ascending order; and perform stratified pressure regulation based on the graded early warning level.

[0016] In one optional implementation, the third processing module can be used to: issue a blue warning when the predicted formation pressure is less than a preset multiple of the warning pressure; issue a yellow warning when the preset multiple of the warning pressure is less than or equal to the predicted formation pressure; issue an orange warning when the predicted formation pressure is greater than or equal to the warning pressure; and issue a red warning when the predicted formation pressure is greater than or equal to the critical formation fracturing pressure.

[0017] In one alternative implementation, the formula for calculating the warning pressure is:

[0018] In the formula, To provide early warning of pressure, For safety reasons, This is the critical fracture pressure of the formation.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] The formation pressure early warning method, device, equipment, and storage medium for carbon dioxide sequestration projects provided in this application can acquire multi-dimensional pressure monitoring data of the surface, wellbore, and formation during carbon dioxide geological sequestration projects, as well as collect formation parameters and carbon dioxide injection parameters during the project. The raw pressure monitoring data is then preprocessed to remove abnormal interference information and calibrate data deviations to obtain standardized pressure data. Combined with a pre-trained formation pressure prediction model, formation pressure prediction results are obtained. Risk levels are then classified based on the predicted pressure values, and graded early warnings are implemented. This enables real-time monitoring, accurate prediction, and graded risk early warning of formation pressure in carbon dioxide geological sequestration, effectively improving the timeliness and accuracy of formation pressure control in the sequestration area and ensuring the safe and stable operation of carbon sequestration projects.

[0023] 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

[0024] 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: Figure 1 This is a schematic flowchart of a formation pressure early warning method for a carbon dioxide sequestration project provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a formation pressure early warning device for a carbon dioxide sequestration project 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

[0025] 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.

[0026] The following description, with reference to the accompanying drawings, describes a method and apparatus for early warning of formation pressure in a carbon dioxide sequestration project according to embodiments of this application.

[0027] Figure 1 This is a schematic flowchart illustrating a formation pressure early warning method for a carbon dioxide sequestration project 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 pressure monitoring data of the ground, wellbore, and formation in the carbon dioxide geological storage area.

[0028] In the embodiments of this application, the pressure monitoring data includes at least one of the following: pore pressure of layered reservoirs, pressure of interlayers, pressure of caprock, wellbore pressure, interlayer interface pressure, wellhead injection pressure, injection rate, pressure change rate, and cumulative injection volume.

[0029] S2. Obtain formation parameters and carbon dioxide injection parameters.

[0030] In the embodiments of this application, the formation parameters include at least one of the following: permeability, porosity, rock strength, geostress, and critical formation fracture pressure; the injection parameters include at least one of the following: injection pressure, discharge rate, temperature, and injection time.

[0031] S3. Preprocess the pressure monitoring data to obtain standard pressure data.

[0032] For example, the raw pressure monitoring data is sequentially cleaned, missing values ​​are filled in, outliers are removed, and data is calibrated and normalized to obtain standard pressure data.

[0033] S4. Based on the current standard pressure data, current formation parameters, and current injection parameters, obtain the formation pressure prediction value through the pre-trained formation pressure prediction model.

[0034] Among them, the formation pressure prediction model is constructed based on convolutional neural networks, long short-term memory networks, and attention mechanisms.

[0035] In one implementation, the training process of the aforementioned formation pressure prediction model includes: extracting several sets of formation parameters, injection parameters, and standard pressure data in a time-series manner based on indoor core experimental data, field monitoring data, numerical simulation data, and historical engineering data, as a training dataset; dividing the training dataset into a training set and a validation set according to a preset ratio; training the formation pressure prediction model using the training set, iterating through the Adam optimizer, with the loss function being the sum of the mean squared error loss and the weighted (risk weight) risk loss term; after the loss function converges, inputting the validation set into the formation pressure prediction model for verification, determining whether the absolute prediction deviation of the formation pressure prediction value is less than a preset threshold; if so, the training of the formation pressure prediction model is completed; otherwise, adjusting the formation pressure prediction model parameters and continuing to train the formation pressure prediction model using the training set.

[0036] S5. Graded early warning based on formation pressure prediction values.

[0037] For example, by combining the safety management standards for carbon dioxide geological storage, different formation pressure threshold ranges and risk levels are defined. The pressure prediction values ​​output by the model are compared with the classification thresholds, and the corresponding level of early warning is automatically triggered according to the degree of exceedance and the level of risk, so as to achieve stratified and precise early warning management of formation pressure risk.

[0038] In one implementation, the aforementioned graded early warning based on formation pressure prediction values ​​includes: obtaining graded early warning levels based on formation pressure prediction values; wherein the graded early warning levels, from low to high, include blue warning, yellow warning, orange warning, and red warning; and performing stratified pressure regulation based on the graded early warning levels.

[0039] In one optional implementation, a graded early warning level is obtained based on the formation pressure prediction value, including: a blue early warning when the formation pressure prediction value is less than a preset multiple of the early warning pressure; a yellow early warning when the preset multiple of the early warning pressure is less than or equal to the formation pressure prediction value; an orange early warning when the formation pressure prediction value is greater than or equal to the early warning pressure; and a red early warning when the formation pressure prediction value is greater than or equal to the formation critical fracturing pressure.

[0040] In one alternative implementation, the formula for calculating the warning pressure is:

[0041] In the formula, To provide early warning of pressure, For safety factor, and , This is the critical fracture pressure of the formation.

[0042] In one optional implementation, after obtaining the graded early warning level, tiered pressure control is performed based on the graded early warning level; the calculation formula for this tiered pressure control is:

[0043] In the formula, For the first Layer injection pressure adjustment amount, For the first Layer regulation coefficient, For the first The measured pressure of the layer, For the first The target security pressure of the layer.

[0044] In some embodiments, pressure regulation can be performed based on graded warnings according to the following steps: Blue warning: Maintain existing injection parameters and perform injection operations at constant pressure; Yellow warning: Reduce the injection flow rate according to the injection pressure adjustment amount of the high-pressure layer to slow down the pressure rise rate, and check the standard pressure data every 10 minutes; Orange warning: Reduce the injection pressure and pump speed according to the injection pressure adjustment amount, and simultaneously start interlayer pressure equalization regulation to make the interlayer pressure difference lower than the crossflow critical value; Red warning: Immediately shut down the injection pump, close the stratified injection valve, and implement formation depressurization.

[0045] In one implementation, if the pressure does not return to the safe range after pressure regulation, the pressure regulation steps are repeated; and the model parameters are optimized based on the pressure prediction model iteratively layered by the regulation data.

[0046] By implementing the embodiments of this application, multi-dimensional pressure monitoring data of the surface, wellbore, and formation can be obtained in carbon dioxide geological storage projects, as well as formation parameters and carbon dioxide injection parameters collected during the project. The raw pressure monitoring data is then preprocessed to remove abnormal interference information and calibrate data deviations, resulting in standardized pressure data. Combined with a pre-trained formation pressure prediction model, formation pressure prediction results are obtained. Risk levels are then classified based on the predicted pressure values, and graded early warnings are implemented. This enables real-time monitoring, accurate prediction, and graded risk early warning of formation pressure in carbon dioxide geological storage, effectively improving the timeliness and accuracy of formation pressure control in the storage area and ensuring the safe and stable operation of carbon storage projects.

[0047] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a formation pressure early warning device for a carbon dioxide sequestration project provided in an embodiment of this application. Figure 2 As shown, the device 200 includes: a first acquisition module 201, used to acquire pressure monitoring data of the surface, wellbore, and formation in the carbon dioxide geological storage area; a second acquisition module 202, used to acquire formation parameters and carbon dioxide injection parameters; a first processing module 203, used to preprocess the pressure monitoring data to acquire standard pressure data; a second processing module 204, used to acquire formation pressure prediction values ​​through a pre-trained formation pressure prediction model based on the current standard pressure data, current formation parameters, and current injection parameters; and a third processing module 205, used to perform graded early warning based on the formation pressure prediction values.

[0048] In one implementation, the pressure monitoring data includes at least one of the following: pore pressure of the layered reservoir, pressure of the interlayer, pressure of the caprock, wellbore pressure, interlayer interface pressure, wellhead injection pressure, injection rate, pressure change rate, and cumulative injection volume; formation parameters include at least one of the following: permeability, porosity, rock strength, geostress, and critical formation fracture pressure; injection parameters include at least one of the following: injection pressure, flow rate, temperature, and injection time.

[0049] In one implementation, the formation pressure prediction model is built based on convolutional neural networks, long short-term memory networks, and attention mechanisms.

[0050] In one implementation, the third processing module 205 can be used to: obtain a graded early warning level based on the predicted formation pressure value; wherein the graded early warning levels, from low to high, include blue early warning, yellow early warning, orange early warning and red early warning; and perform stratified pressure regulation based on the graded early warning level.

[0051] In one optional implementation, the third processing module 205 can be used to: issue a blue warning when the predicted formation pressure is less than the warning pressure at a preset ratio; issue a yellow warning when the warning pressure at the preset ratio is less than or equal to the predicted formation pressure; issue an orange warning when the predicted formation pressure is greater than or equal to the warning pressure; and issue a red warning when the predicted formation pressure is greater than or equal to the critical formation fracturing pressure.

[0052] In one alternative implementation, the formula for calculating the warning pressure is:

[0053] In the formula, To provide early warning of pressure, For safety factor, and , This is the critical fracture pressure of the formation.

[0054] The apparatus described in this application can acquire multi-dimensional pressure monitoring data of the surface, wellbore, and formation during carbon dioxide geological storage projects, as well as collected formation parameters and carbon dioxide injection parameters during the project. The raw pressure monitoring data is then preprocessed to remove abnormal interference and calibrate data deviations, resulting in standardized pressure data. Combined with a pre-trained formation pressure prediction model, formation pressure prediction results are obtained. Risk levels are then categorized based on the predicted pressure values, and graded early warnings are implemented. This allows for real-time monitoring, accurate prediction, and graded risk early warning of formation pressure in carbon dioxide geological storage, effectively improving the timeliness and accuracy of formation pressure control in the storage area and ensuring the safe and stable operation of carbon storage projects.

[0055] It should be noted that the explanation of the aforementioned embodiment of the formation pressure early warning method for carbon dioxide sequestration projects also applies to the formation pressure early warning device for carbon dioxide sequestration projects in this embodiment, and will not be repeated here.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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 formation pressure warning in carbon dioxide storage projects, characterized by, include: Acquire pressure monitoring data of the surface, wellbore, and formation in the carbon dioxide geological sequestration area; Obtain formation parameters and carbon dioxide injection parameters; The pressure monitoring data is preprocessed to obtain standard pressure data; Based on the current standard pressure data, current formation parameters, and current injection parameters, the formation pressure prediction value is obtained through a pre-trained formation pressure prediction model. A graded early warning system is implemented based on the predicted formation pressure values.

2. The method of claim 1, wherein, The pressure monitoring data includes at least one of the following: pore pressure of layered reservoirs, pressure of interlayers, pressure of caprock, wellbore pressure, interlayer interface pressure, wellhead injection pressure, injection rate, pressure change rate, and cumulative injection volume; The formation parameters include at least one of the following: permeability, porosity, rock strength, geostress, and critical formation fracture pressure; The injection parameters include at least one of the following: injection pressure, flow rate, temperature, and injection time.

3. The method according to claim 1, characterized in that, The formation pressure prediction model is constructed based on convolutional neural networks, long short-term memory networks, and attention mechanisms.

4. The method of claim 1, wherein, The graded early warning based on the predicted formation pressure includes: The graded early warning levels are obtained based on the predicted formation pressure values; wherein, the graded early warning levels, from low to high, include blue warning, yellow warning, orange warning, and red warning. Based on the aforementioned graded early warning levels, tiered pressure regulation will be implemented.

5. The method of claim 4, wherein, The step of obtaining the graded early warning level based on the predicted formation pressure value includes: A blue warning is issued when the predicted formation pressure is less than a preset multiple of the warning pressure. A yellow alert is issued when the pre-set warning pressure is less than or equal to the predicted formation pressure and less than the warning pressure. An orange alert will be issued when the predicted formation pressure is greater than or equal to the warning pressure. A red alert is issued when the predicted formation pressure is greater than or equal to the critical formation fracturing pressure.

6. The method of claim 5, wherein, The formula for calculating the warning pressure is: wherein is the pre-alarm pressure, is the safety factor, is the critical formation breakdown pressure.

7. A formation pressure warning device for a carbon dioxide sequestration project, characterized by, include: The first acquisition module is used to acquire pressure monitoring data of the ground, wellbore, and formation in the carbon dioxide geological storage area; The second acquisition module is used to acquire formation parameters and carbon dioxide injection parameters; The first processing module is used to preprocess the pressure monitoring data to obtain standard pressure data; The second processing module is used to obtain the predicted formation pressure value based on the current standard pressure data, current formation parameters, and current injection parameters using a pre-trained formation pressure prediction model. The third processing module is used to perform graded early warning based on the predicted formation pressure values.

8. An electronic device, comprising: 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 having stored therein instructions, the storage medium comprising: 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, 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.

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