Method and device for evaluating carbon sequestration leakage state

Abstract

The invention discloses a carbon sequestration leakage state evaluation method and device, and relates to the field of carbon sequestration, and the method comprises the steps: obtaining the injection data corresponding to carbon dioxide, and the injection data comprises at least one of the following: the injection speed, the injection pressure, the injection position, and the injection quality; on the basis of a target prediction model, according to the injection data, the sealing damage state of a shaft, the sealing damage state of a cover layer and the sealing damage state of a fault are predicted, and the shaft, the cover layer and the fault are used for sealing the carbon dioxide; and predicting the leakage state of the carbon dioxide according to the sealing damage state of the shaft, the sealing damage state of the cover layer and the sealing damage state of the fault. By adopting the technical scheme, the problem that the prediction efficiency and accuracy of the leakage state of the carbon dioxide are relatively low is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of carbon sequestration, and in particular, to a method and device for evaluating the leakage state of carbon sequestration. BACKGROUND

[0002] With the acceleration of global industrialization, the emission of carbon dioxide (CO2) is increasing, which has a significant impact on the environment and climate change. In order to reduce greenhouse gas emissions, governments and enterprises are seeking effective carbon dioxide capture, storage and utilization technologies. However, there is a risk of leakage during the capture, transportation and storage of carbon dioxide, which not only may cause environmental pollution, but also may cause safety accidents.

[0003] In order to avoid the above problems, it is necessary to evaluate the leakage risk in the overall process of sequestrating carbon dioxide, and in the existing technology, the leakage state of carbon dioxide sequestration is mainly predicted by relying on sensor networks and cloud computing platforms. These technologies have improved the safety and efficiency of carbon dioxide sequestration to some extent, but at the same time, there are some limitations, such as data processing delay, accuracy of leakage location information, high monitoring cost and reliability problems in complex environments.

[0004] In view of the problem in the related art that the efficiency and accuracy of predicting the leakage state of carbon dioxide are low, no effective solution has been proposed so far.

[0005] Therefore, it is necessary to improve the related art to overcome the defects in the related art. SUMMARY

[0006] The embodiments of the present application provide a method and device for evaluating the leakage state of carbon sequestration to at least solve the problem that the efficiency and accuracy of predicting the leakage state of carbon dioxide are low.

[0007] According to an aspect of an embodiment of the present application, a method for evaluating the leakage state of carbon sequestration is provided, comprising: obtaining injection data corresponding to carbon dioxide, wherein the injection data includes at least one of the following: injection speed, injection pressure, injection location, injection quality; based on a target prediction model, predicting the sealing damage state of a wellbore, the sealing damage state of a cap layer and the sealing damage state of a fault based on the injection data, wherein the wellbore, the cap layer and the fault are used to sequester the carbon dioxide; and predicting the leakage state of the carbon dioxide based on the sealing damage state of the wellbore, the sealing damage state of the cap layer and the sealing damage state of the fault.

[0008] In an example embodiment, predicting a sealing damage state of a wellbore based on a target prediction model according to the injection data comprises: predicting a casing equivalent stress of the wellbore based on the injection pressure according to the target prediction model; determining that the sealing damage state of the wellbore is a damage state in a case that the casing equivalent stress is greater than or equal to a preset casing internal yield pressure; and / or predicting a near-wellbore effective stress of the wellbore based on the injection velocity according to the target prediction model; determining that the sealing damage state of the wellbore is a damage state in a case that the near-wellbore effective stress is greater than or equal to a first preset fracture pressure.

[0009] In an example embodiment, predicting a sealing damage state of a cap rock based on a target prediction model according to the injection data comprises: predicting a bearing pressure of the cap rock based on the injection velocity, the injection pressure, the injection location and the injection mass according to the target prediction model; determining that the sealing damage state of the cap rock is a damage state in a case that the bearing pressure of the cap rock is greater than or equal to a second preset fracture pressure; and / or predicting a strain displacement near the wellbore based on the injection velocity, the injection location and the injection mass according to the target prediction model; determining that the sealing damage state of the cap rock is a damage state in a case that the strain displacement near the wellbore is greater than or equal to a preset threshold; and / or obtaining a permeability and a configuration file of the cap rock, and obtaining a breakthrough pressure of the cap rock according to the permeability of the cap rock and the configuration file; predicting a viscous drag of the carbon dioxide based on the injection location, the injection velocity and the injection mass according to the target prediction model; determining that the sealing damage state of the cap rock is a damage state in a case that the viscous drag of the carbon dioxide is greater than or equal to the breakthrough pressure of the cap rock.

[0010] In an example embodiment, predicting a sealing damage state of a fault based on a target prediction model according to the injection data comprises: predicting a pressure on the fault based on the injection location, the injection velocity and the injection mass according to the target prediction model; determining that the sealing damage state of the fault is a damage state in a case that the pressure on the fault is greater than or equal to a preset opening pressure.

[0011] In an example embodiment, predicting a leakage state of the carbon dioxide according to the sealing damage state of the wellbore, the sealing damage state of the cap rock and the sealing damage state of the fault comprises: predicting the leakage state of the carbon dioxide according to the sealing damage state of the wellbore, the sealing damage state of the cap rock and the sealing damage state of the fault to obtain a target leakage state; wherein the target leakage state comprises a target leakage location and a target leakage mass.

[0012] In one example embodiment, the method further comprises: after the carbon dioxide is injected, obtaining a leakage mass of the carbon dioxide within a preset time to obtain a first leakage mass; and in a case where the first leakage mass is greater than a target leakage mass, sending an alarm message and checking a degree of damage to the sealing of the wellbore, a degree of damage to the sealing of the cap rock, and a degree of damage to the sealing of the fault.

[0013] According to another aspect of the embodiments of the present application, an evaluation device for a leakage state of carbon sequestration is further provided, comprising: an obtaining module configured to obtain injection data corresponding to carbon dioxide, wherein the injection data comprises at least one of injection speed, injection pressure, injection position, and injection mass; a first prediction module configured to predict a sealing damage state of a wellbore, a sealing damage state of a cap rock, and a sealing damage state of a fault based on a target prediction model and according to the injection data, wherein the wellbore, the cap rock, and the fault are used to sequester the carbon dioxide; and a second prediction module configured to predict a leakage state of the carbon dioxide according to the sealing damage state of the wellbore, the sealing damage state of the cap rock, and the sealing damage state of the fault.

[0014] According to still another aspect of the embodiments of the present application, a computer readable storage medium having a computer program stored therein is further provided, wherein the computer program is configured to execute the above-mentioned evaluation method for a leakage state of carbon sequestration when running.

[0015] According to still another aspect of the embodiments of the present application, an electronic device is further provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned evaluation method for a leakage state of carbon sequestration through the computer program.

[0016] According to still another aspect of the embodiments of the present application, a computer program product is further provided, comprising a computer program, which, when executed by a processor, executes the above-mentioned evaluation method for a leakage state of carbon sequestration.

[0017] According to the present application, injection data corresponding to carbon dioxide is obtained, a target prediction model is used to predict sealing damage states of a wellbore, a cap rock, and a fault according to the obtained injection data, and a leakage state of the carbon dioxide is predicted according to the predicted sealing damage states of the wellbore, the cap rock, and the fault. Since the injection data comprises at least one of injection speed, injection pressure, injection position, and injection mass corresponding to the carbon dioxide, and the sealing damage states of the wellbore, the cap rock, and the fault are predicted, the accuracy of predicting the leakage state of the carbon dioxide is improved, and the target prediction model is used for prediction, thereby improving the efficiency of predicting the leakage state of the carbon dioxide, and further solving the problem of low efficiency and accuracy in predicting the leakage state of the carbon dioxide. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a hardware structure block diagram of a mobile terminal for evaluating the leakage status of carbon sequestration according to an embodiment of this application.

[0021] Figure 2 This is a flowchart of a method for evaluating the leakage status of carbon sequestration according to an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of a wellbore leakage location according to an embodiment of this application;

[0023] Figure 4 This is a structural block diagram of a carbon sequestration leakage status evaluation device according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for an evaluation method of carbon sequestration leakage status according to an embodiment of this application. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor (MCU) or a field-programmable gate array (FPGA)) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0027] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to a method for evaluating the leakage state of carbon sequestration in this embodiment of the application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0029] An evaluation method of a carbon sequestration leakage state is provided in the embodiment, Figure 2 is a flowchart of an evaluation method of a carbon sequestration leakage state according to the embodiment of the application, as shown in the figure, the flow includes the following steps S202 to S206: Figure 2

[0030] Step S202: Obtain injection data corresponding to carbon dioxide, wherein the injection data includes at least one of the following: injection speed, injection pressure, injection position, injection quality;

[0031] Optionally, the injection data related to CO2 is obtained from the operation records of the CO2 sequestration project. These data are the basis for evaluating the sequestration condition and can reflect the leakage state of CO2 and the degree of influence on the sealing structure (such as wellbore, caprock and fault).

[0032] Step S204: Based on a target prediction model, predict the sealing damage state of the wellbore, the sealing damage state of the caprock and the sealing damage state of the fault according to the injection data, wherein the wellbore, the caprock and the fault are used to sequester the carbon dioxide;

[0033] Optionally, the target prediction model is a general neural network model obtained by supervised learning, taking the injection data obtained in step S202 as input, and predicting the sealing damage state of the wellbore, the caprock and the fault in the CO2 sequestration process.

[0034] It should be noted that the wellbore penetrates the caprock to reach the sequestration layer, and the fault may cross or be adjacent to this structure, affecting the safety of CO2 sequestration, and the three together prevent gas from leaking upward.

[0035] Step S206: According to the sealing damage state of the wellbore, the sealing damage state of the caprock and the sealing damage state of the fault, predict the leakage state of the carbon dioxide.

[0036] Optionally, in step S206, the sealing damage probability output by the target prediction model is integrated and analyzed, combined with the real-time monitored gas concentration data and position information, to evaluate the safety of CO2 sequestration in multiple dimensions.

[0037] It should be noted that through the implementation of steps S202 to S206, the technical scheme of the present application not only improves the speed of the leakage risk evaluation of the CO2 sequestration project, but also improves the accuracy and comprehensiveness of the evaluation through the prediction model, providing strong technical support for the safe operation of the project and environmental protection, and realizing intelligent, efficient and safe monitoring of the CO2 sequestration project.

[0038] ​The above steps acquire carbon dioxide injection data. A target prediction model is then used to predict the sealing failure state of the wellbore, caprock, and fault based on this data. This prediction, in turn, predicts the carbon dioxide leakage state. Since the injection data includes at least one of the following: injection rate, injection pressure, injection location, or injection mass, and it predicts the sealing failure state of the wellbore, caprock, and fault, the accuracy of predicting carbon dioxide leakage is improved. Furthermore, the use of a target prediction model increases the efficiency of predicting carbon dioxide leakage, thus addressing the problem of low efficiency and accuracy in predicting carbon dioxide leakage.

[0039] In an exemplary embodiment, predicting the wellbore sealing failure state based on the injection data using the target prediction model can be achieved through the following steps S11-S12 and / or steps S13-S14:

[0040] Step S11: Based on the target prediction model, predict the casing equivalent stress of the wellbore according to the injection pressure;

[0041] Step S12: When the equivalent stress of the casing is greater than or equal to the preset yield pressure inside the casing, the sealing failure state of the wellbore is determined to be a failure state.

[0042] Optionally, the target prediction model can predict the casing equivalent stress of the wellbore based on the main chart pressure. For example, when the injection pressure is 10 MPa, the casing equivalent stress of the wellbore is 12.68 MPa. Under the condition that the preset yield pressure inside the casing is 50 MPa, the sealing failure state of the wellbore is an undamaged state.

[0043] Optionally, when the equivalent stress of the casing is 70 MPa and the preset yield pressure inside the casing is 60 MPa, the sealing failure state of the wellbore is a failure state.

[0044] It should be noted that casing equivalent stress is an indicator that measures the stress on the casing under multiaxial stress, equivalent to the stress under uniaxial tension or compression. During high-pressure CO2 injection, the well casing is subjected to enormous internal pressure, which may lead to plastic deformation or failure of the material. The target prediction model predicts the casing equivalent stress during CO2 injection by analyzing the injection pressure and casing material characteristics, providing a quantitative basis for assessing the casing's load-bearing capacity.

[0045] It should be noted that the preset yield pressure inside the casing is determined based on the yield strength of the casing material and design standards. When the predicted equivalent stress of the casing reaches or exceeds this preset value, it indicates that the casing material may have reached or is close to its yield limit, posing a risk of plastic deformation or failure, which could potentially lead to CO2 leakage. This assessment allows for timely warning of potential safety issues in the storage structure, providing a basis for preventative maintenance and emergency response decisions.

[0046] Step S13: Based on the target prediction model, predict the near-wellbore effective stress of the wellbore according to the injection rate;

[0047] Step S14: When the effective stress near the well is greater than or equal to the first preset rupture pressure, the sealing failure state of the wellbore is determined to be a failure state.

[0048] Optionally, the first preset burst pressure is 14 MPa.

[0049] Optionally, the near-wellbore effective stress can reflect the formation pressure. The greater the gas injection rate, the greater the near-wellbore effective stress and the greater the formation pressure. When the near-wellbore effective stress exceeds the first preset rupture pressure, the wellbore's sealing performance is compromised.

[0050] It should be noted that near-wellbore effective stress refers to the stress exerted on the formation due to changes in fluid pressure in the region near the CO2 injection wellbore. The injection rate affects the pressure distribution in the formation around the wellbore, thus influencing the magnitude of the near-wellbore effective stress. The target prediction model analyzes the relationship between injection rate and formation stress to predict the near-wellbore effective stress at different injection rates, providing a scientific basis for adjusting injection parameters and optimizing the sealing process.

[0051] It should be noted that the first preset fracturing pressure is determined based on the rock mechanical properties of the sealing layer and caprock, representing the critical stress value at which the formation material begins to fracture. When the predicted near-wellbore effective stress reaches or exceeds the first preset fracturing pressure, it indicates that the stability of the formation near the wellbore is severely threatened, posing a high risk of fracturing and CO2 leakage. This assessment provides early warning of potential geological structural damage, offering crucial information for taking measures to ensure the operational safety and environmental protection of the sealing project.

[0052] It should be noted that, through the implementation of steps S11 to S14 above, the embodiments of this application can not only perform accurate risk assessment based on injected data, but also provide timely early warning of potential safety issues in the storage structure through intelligent analysis using predictive models. This predictive method not only improves the monitoring efficiency and accuracy of CO2 storage projects, but also dynamically adjusts storage parameters, optimizes the storage process, and reduces leakage risks, providing strong technical support for the safe operation and environmental protection of storage projects.

[0053] In an exemplary embodiment, predicting the sealing failure state of the cap layer based on the injected data, according to the target prediction model, can be achieved through the following steps S21-S22 and / or steps S23-S24 and / or steps S25-S27:

[0054] Step S21: Based on the target prediction model, predict the pressure that the capping layer can withstand according to the injection rate, the injection pressure, the injection location, and the injection quality;

[0055] Step S22: When the pressure on the cover layer is greater than or equal to the second preset rupture pressure, the sealing failure state of the cover layer is determined to be a failure state;

[0056] Optionally, different materials for the capping layer correspond to different second preset burst pressures, which can be 18 MPa.

[0057] Optionally, the target prediction model can assess the pressure exerted by the caprock under different injection conditions, which is a key parameter for determining the risk of caprock rupture. The second preset rupture pressure is a critical value determined based on the mechanical properties of the caprock material, representing the pressure threshold at which the caprock begins to rupture. If the predicted pressure reaches or exceeds this threshold, it indicates an extremely high risk of caprock rupture, requiring immediate action.

[0058] Step S23: Based on the target prediction model, predict the strain displacement near the wellbore according to the injection rate, the injection location, and the injection mass;

[0059] Step S24: If the strain displacement near the wellbore is greater than or equal to a preset threshold, the sealing failure state of the capping layer is determined to be a failure state;

[0060] Optionally, the greater the injection speed, the closer the injection location is to the caprock, and the greater the injection mass, the greater the strain displacement near the wellbore. The target prediction model can predict the specific value of the strain displacement generated near the wellbore based on the specific value of the injection data. The increase in strain displacement may indicate that the pressure on the formation exceeds its bearing capacity, thereby affecting the sealing performance of the caprock.

[0061] Optionally, the preset threshold is set according to the geological conditions and engineering requirements of the caprock. If the strain displacement exceeds this threshold, it may indicate that the sealing performance of the caprock has decreased and CO2 may leak.

[0062] Step S25: Obtain the permeability and configuration file of the caprock, and obtain the breakthrough pressure of the caprock based on the permeability and configuration file;

[0063] Step S26: Based on the target prediction model, predict the viscous resistance of the carbon dioxide according to the injection location, the injection speed, and the injection mass;

[0064] Step S27: If the viscous resistance of the carbon dioxide is greater than or equal to the breakthrough pressure of the capping layer, the sealing failure state of the capping layer is determined to be a failure state.

[0065] Optionally, the configuration file has a correspondence between permeability and caprock breakthrough pressure, wherein the permeability and caprock breakthrough pressure are in one-to-one correspondence, and the higher the permeability of the caprock, the lower the caprock breakthrough pressure.

[0066] It should be noted that viscous resistance is the flow resistance encountered by CO2 when flowing through the caprock, and it is related to the injection parameters and caprock characteristics. The target prediction model can predict the flow resistance of CO2 under given injection conditions. When the viscous resistance of carbon dioxide is greater than or equal to the breakthrough pressure of the caprock, it indicates that CO2 may penetrate the caprock, leading to a decrease in sealing performance and a risk of leakage.

[0067] It should be noted that the implementation of steps S21 to S27 above, by combining CO2 injection data, the physical characteristics of the caprock, and the target prediction model, can not only monitor the sealing performance of the caprock but also predict potential rupture risks, providing comprehensive technical assurance for the safe operation of CO2 sequestration projects. Through early warning, engineers can take timely measures to optimize sequestration parameters, ensuring the environmental safety and economic benefits of the project. Furthermore, this predictive method can promote continuous improvement of sequestration technology, enhancing the overall efficiency and safety of CO2 sequestration.

[0068] In an exemplary embodiment, predicting the sealing failure state of the fault based on the injected data using the target prediction model can be achieved through the following steps S31-S32:

[0069] Step S31: Based on the target prediction model, predict the pressure on the fault according to the injection location, the injection rate, and the injection quality;

[0070] Step S32: When the pressure on the fault is greater than or equal to the preset opening pressure, the sealing failure state of the fault is determined to be a failure state.

[0071] Optionally, the distance to the fault can be obtained based on the injection location. The closer to the fault, the higher the injection speed, and the greater the injection quality, the greater the pressure on the fault. By analyzing these injection data, the target prediction model can predict the pressure on the fault during CO2 injection and assess its potential opening or sliding risk.

[0072] It should be noted that the preset opening pressure is set based on the geological conditions and mechanical properties of the fault, representing the pressure threshold at which the fault begins to move or open. If the predicted pressure value reaches or exceeds this preset value, it means that the stability of the fault is seriously threatened, and CO2 may leak through the fault, posing a threat to the environment and the safety of the containment project. Faults of different materials have different fault opening pressures, and the preset opening pressure can be 11 MPa.

[0073] Optionally, by comparing the pressure with the preset opening pressure, the potential risk of fault opening can be promptly warned, providing a real-time monitoring and early warning mechanism for the safe operation of the sealing project. Once the pressure is detected to have reached the preset opening pressure, measures can be taken immediately, such as suspending or adjusting CO2 injection, to prevent leakage and ensure the long-term safety and environmental friendliness of the sealing project.

[0074] It should be noted that the implementation of steps S31 to S32 above, by using a target prediction model combined with CO2 injection data, enables real-time assessment and risk prediction of fault sealing performance. This method not only improves the accuracy and timeliness of monitoring but also provides a scientific basis for risk management and safety control of CO2 sequestration projects, which is of great significance for ensuring the safe and stable operation of sequestration projects and protecting the environment. Through early warning, engineers can take preventative measures to avoid or reduce potential environmental hazards. At the same time, this method also contributes to the continuous optimization and improvement of sequestration technology, enhancing the overall efficiency and safety of CO2 sequestration.

[0075] In an exemplary embodiment, predicting the carbon dioxide leakage state based on the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault includes the following steps: predicting the carbon dioxide leakage state based on the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault to obtain a target leakage state; wherein, the target leakage state includes: target leakage location and target leakage mass.

[0076] Optionally, a comprehensive assessment result is formed by combining the predicted sealing failure states of the wellbore, caprock, and fault. The sealing failure state here encompasses the changes in pressure, stress, and displacement that the wellbore, caprock, and fault may experience during high-pressure injection, as well as a comparison of these changes with preset safety thresholds. Based on the comprehensive assessment results, a reference prediction model and / or prediction algorithm are used to analyze whether high-pressure CO2 is likely to leak from weak points in the wellbore, caprock, or fault. Through complex mathematical models and algorithms, the probability, location, and quality of CO2 leakage are predicted.

[0077] It should be noted that the target leak location refers to the most likely leak point predicted, which could be a part of the wellbore, a crack in the caprock, or a reactivation zone of a fault. The target leak mass reflects the magnitude of CO2 leakage that may occur under specific conditions, which helps in assessing the environmental and economic impact of the leak.

[0078] It should be noted that by comprehensively analyzing the sealing failure status of the wellbore, caprock, and fault, the leakage status of carbon dioxide can be predicted. This embodiment of the application provides more accurate leak warnings, avoiding the limitations of a single monitoring point and improving the overall performance and response speed of the monitoring system. The prediction of the target leakage status, especially the determination of the leak location and leakage quality, provides crucial information for emergency response. Once a leakage risk is predicted, engineers can quickly locate the leak point and take targeted measures, such as sealing, pressure reduction, or emergency evacuation, to reduce environmental damage and economic losses. Furthermore, the predicted leakage status can be used to guide the design and operational optimization of the CO2 storage project. By adjusting injection parameters and reinforcing the wellbore or caprock structure, the leakage risk can be reduced, and the CO2 storage efficiency and safety can be improved.

[0079] In an exemplary embodiment, the method further includes the following steps S41-S42:

[0080] Step S41: After injecting the carbon dioxide, within a preset time, obtain the leakage mass of the carbon dioxide to obtain the first leakage mass;

[0081] Optionally, after the carbon dioxide (CO2) injection is completed, the system enters the monitoring phase. Within a preset time window (e.g., the first 24 hours after injection), the gas concentration in the target area is continuously monitored, and the actual leakage mass of carbon dioxide, i.e., the first leakage mass, is calculated through an analysis algorithm to assess the safety status of the sealing project in real time.

[0082] Step S42: If the first leakage mass is greater than the target leakage mass, send an alarm message and check the degree of sealing damage of the wellbore, the degree of sealing damage of the cap layer, and the degree of sealing damage of the fault, wherein the target leakage state includes the target leakage mass.

[0083] Optionally, if the initial leak detected by the monitoring system exceeds the previously predicted target leak quality, the system will automatically trigger an alarm mechanism, sending an alarm message to engineers. Simultaneously, the system will further examine and assess the degree of seal damage to the wellbore, caprock, and fault to determine the specific cause and location of the leak, helping engineers take effective remedial measures. This process may include updating the geological model, on-site inspection, and data analysis to gain a comprehensive understanding of the leak.

[0084] It should be noted that, by combining steps S41 and S42, this embodiment of the application can monitor the leakage status of the CO2 sequestration project in real time, respond quickly to abnormal situations, and ensure project safety and environmental protection. This monitoring mechanism not only improves CO2 sequestration efficiency and reduces greenhouse gas emissions, but also provides a scientific basis for the continuous optimization of the sequestration project through data analysis and on-site inspection, playing an important role in promoting the development and application of carbon sequestration technology.

[0085] Obviously, the embodiments described above are merely some embodiments of the present invention, and not all embodiments. To better understand the above method, the following description, in conjunction with embodiments, illustrates the process, but is not intended to limit the technical solutions of the embodiments of the present invention. Specifically:

[0086] As an optional implementation method, in general, it involves identifying various factors that could cause leakage in the sealed area, setting relevant judgment parameters and conditions, obtaining preliminary simulation data, inputting the preliminary simulation data (i.e., the aforementioned injected data) into the large prediction model, performing leakage simulation, and thereby estimating the location of the leakage point based on the data and the volume of the sealed area, thus achieving key monitoring of key locations.

[0087] Optionally, refer to Figure 3 Assuming there are six main risk points for wellbore leakage, primarily related to casing material, cement quality, and stress changes near the wellbore, based on the geomechanical coupling model, after injecting CO2 at the maximum actual gas production rate, the effective stress within 3 meters of the wellbore changes significantly, with a maximum change of approximately 1.5 MPa, which does not reach the rupture pressure. Therefore, the leakage risk in the annular region between the formation and the cement is low.

[0088] Optionally, the above wellbore analysis can determine the changes in wellbore parameters by establishing a near-wellbore model, obtaining the actual gas injection rate curve of the current wellbore, and comparing Mohr's circle curves during injection; without considering the wellbore material and cement quality, the stress changes in the wellbore, caprock, and fault during the actual gas injection process can be simulated using a geomechanical coupling model to analyze the factors affecting leakage risk.

[0089] (1) Gas injection rate: As the gas injection rate increases, the bottom hole pressure increases, resulting in a decrease in effective stress, an increase in the leftward shift of the Mohr circle, and an increase in the risk of rupture and leakage.

[0090] (2) Location of injection well: Near the location of the injection well, the effective stress decreases significantly, resulting in a large strain displacement near the injection well.

[0091] (3) Cap layer permeability: When the cap layer permeability is high, the breakthrough pressure is low, which can cause CO2 to break through along the cap layer.

[0092] Secondly, there is a certain risk of deformation due to leakage of the caprock. For example, after 10 years of injection, the effective stress of the caprock decreases by a maximum of 0.68 MPa, the Mohr circle shifts to the left, and the critical rupture point is not reached, resulting in a maximum longitudinal displacement of 0.04 meters in the caprock near the injection location; after 20 years of injection, the effective stress of the caprock decreases by a maximum of 0.66 MPa, and the critical rupture point is not reached, resulting in a maximum longitudinal displacement of 0.035 meters in the caprock near the injection location.

[0093] Optionally, by setting monitoring standards, when the actual monitoring results exceed the standards, it indicates that there is a risk of deformation leakage.

[0094] It should be noted that the risk of leakage from the caprock also includes the risk of breakthrough. For example, if the caprock is sealed for 100 years, less than 1% of the CO2 will be contained in the No. 1 caprock, distributed in a point-like pattern, with a breakthrough area of ​​1.4 square kilometers, indicating a low risk of leakage. Therefore, the breakthrough situation of the caprock can be determined by observing its distribution.

[0095] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0096] This embodiment also provides a device for evaluating the leakage status of carbon sequestration. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] Figure 4 This is a structural block diagram of a carbon sequestration leakage status evaluation device according to an embodiment of this application. The device includes:

[0098] The acquisition module 402 is used to acquire injection data corresponding to carbon dioxide, wherein the injection data includes at least one of the following: injection speed, injection pressure, injection location, and injection mass;

[0099] The first prediction module 404 is used to predict the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault based on the target prediction model and the injected data, wherein the wellbore, the caprock, and the fault are used to seal the carbon dioxide.

[0100] The second prediction module 406 is used to predict the carbon dioxide leakage state based on the sealing failure state of the wellbore, the sealing failure state of the cap layer, and the sealing failure state of the fault.

[0101] The aforementioned carbon sequestration leakage status evaluation device acquires injection data corresponding to carbon dioxide. Using a target prediction model, it predicts the sealing failure status of the wellbore, caprock, and fault based on the acquired injection data, thereby predicting the carbon dioxide leakage status based on the predicted sealing failure status of the wellbore, caprock, and fault. Since the injection data includes at least one of the following: injection rate, injection pressure, injection location, and injection mass, and predicts the sealing failure status of the wellbore, caprock, and fault, the accuracy of predicting carbon dioxide leakage status is improved. Furthermore, the use of a target prediction model improves the efficiency of predicting carbon dioxide leakage status, thus solving the problem of low efficiency and accuracy in predicting carbon dioxide leakage status.

[0102] In an exemplary embodiment, the first prediction module 404 is further configured to predict the casing equivalent stress of the wellbore based on the target prediction model and the injection pressure; determine the wellbore's sealing failure state as a failure state if the casing equivalent stress is greater than or equal to a preset casing yield pressure; and / or predict the near-wellbore effective stress of the wellbore based on the target prediction model and the injection rate; determine the wellbore's sealing failure state as a failure state if the near-wellbore effective stress is greater than or equal to a first preset rupture pressure.

[0103] In an exemplary embodiment, the first prediction module 404 is further configured to: predict the pressure the caprock can withstand based on the target prediction model, according to the injection rate, the injection pressure, the injection location, and the injection mass; determine the caprock's sealing failure state as a failure state if the caprock's pressure withstands a pressure greater than or equal to a second preset rupture pressure; and / or predict the strain displacement near the wellbore based on the target prediction model, according to the injection rate, the injection location, and the injection mass; determine the caprock's sealing failure state as a failure state if the strain displacement near the wellbore is greater than or equal to a preset threshold; and / or obtain the caprock's permeability and configuration file, and obtain the caprock's breakthrough pressure based on the caprock's permeability and configuration file; predict the viscous resistance of the carbon dioxide based on the target prediction model, according to the injection location, the injection rate, and the injection mass; determine the caprock's sealing failure state as a failure state if the carbon dioxide's viscous resistance is greater than or equal to the caprock's breakthrough pressure.

[0104] In an exemplary embodiment, the first prediction module 404 is further configured to predict the pressure on the fault based on the target prediction model, according to the injection location, the injection speed, and the injection quality; and determine the fault's sealing failure state as a failure state when the pressure on the fault is greater than or equal to a preset opening pressure.

[0105] In an exemplary embodiment, the second prediction module 406 is further configured to predict the leakage state of the carbon dioxide based on the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault, thereby obtaining a target leakage state; wherein the target leakage state includes: target leakage location and target leakage mass.

[0106] In an exemplary embodiment, the above-mentioned device further includes: a detection module, configured to, within a preset time after the carbon dioxide is injected, acquire the leakage mass of the carbon dioxide to obtain a first leakage mass; and, if the first leakage mass is greater than the target leakage mass, send an alarm message and check the degree of sealing damage of the wellbore, the degree of sealing damage of the cap layer, and the degree of sealing damage of the fault, wherein the target leakage state includes the target leakage mass.

[0107] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when run.

[0108] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0109] S1, Obtain the injection data corresponding to carbon dioxide, wherein the injection data includes at least one of the following: injection speed, injection pressure, injection location, and injection mass;

[0110] S2, based on the target prediction model, predict the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault according to the injection data, wherein the wellbore, the caprock, and the fault are used to seal the carbon dioxide;

[0111] S3, based on the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault, predict the leakage state of the carbon dioxide.

[0112] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0113] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0114] Embodiments of this application also provide a computer program product, including a computer program, wherein the computer program, when executed by a processor, performs the steps in any of the above method embodiments.

[0115] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0116] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0117] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0118] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for evaluating the leakage status of carbon sequestration, characterized in that, include: Obtain injection data corresponding to carbon dioxide, wherein the injection data includes at least one of the following: injection rate, injection pressure, injection location, and injection mass; Based on the target prediction model, the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault are predicted according to the injection data, wherein the wellbore, the caprock, and the fault are used to seal the carbon dioxide; The leakage status of carbon dioxide is predicted based on the sealing failure status of the wellbore, the sealing failure status of the caprock, and the sealing failure status of the fault.

2. The method according to claim 1, characterized in that, Based on the target prediction model, the wellbore sealing failure state is predicted according to the injected data, including: Based on the target prediction model, the casing equivalent stress of the wellbore is predicted according to the injection pressure; if the casing equivalent stress is greater than or equal to the preset casing yield pressure, the wellbore's sealing failure state is determined to be a failure state; and / or Based on the target prediction model, the near-wellbore effective stress of the wellbore is predicted according to the injection rate; if the near-wellbore effective stress is greater than or equal to the first preset rupture pressure, the sealing failure state of the wellbore is determined to be a failure state.

3. The method according to claim 1, characterized in that, Based on the target prediction model, the sealing failure state of the cap layer is predicted according to the injected data, including: Based on the target prediction model, the pressure that the capping layer can withstand is predicted according to the injection rate, the injection pressure, the injection location, and the injection mass; if the pressure that the capping layer can withstand is greater than or equal to a second preset rupture pressure, the sealing failure state of the capping layer is determined to be a failure state; and / or Based on the target prediction model, the strain displacement near the wellbore is predicted according to the injection rate, the injection location, and the injection mass; if the strain displacement near the wellbore is greater than or equal to a preset threshold, the sealing failure state of the caprock is determined to be a failure state; and / or Obtain the permeability and configuration file of the capping layer, and obtain the breakthrough pressure of the capping layer based on the permeability and configuration file; predict the viscous resistance of the carbon dioxide based on the target prediction model, according to the injection location, the injection rate and the injection mass; if the viscous resistance of the carbon dioxide is greater than or equal to the breakthrough pressure of the capping layer, determine the sealing failure state of the capping layer as a failure state.

4. The method according to claim 1, characterized in that, Based on the target prediction model, the sealing failure state of the fault is predicted according to the injected data, including: Based on the target prediction model, the pressure on the fault is predicted according to the injection location, the injection rate, and the injection quality; When the pressure on the fault is greater than or equal to the preset opening pressure, the sealing failure state of the fault is determined to be a failure state.

5. The method according to claim 1, characterized in that, Based on the sealing failure status of the wellbore, the sealing failure status of the caprock, and the sealing failure status of the fault, the leakage status of the carbon dioxide is predicted, including: Based on the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault, the leakage state of the carbon dioxide is predicted, and the target leakage state is obtained. The target leakage status includes: the target leakage location and the target leakage mass.

6. The method according to claim 1, characterized in that, The method further includes: After the carbon dioxide is injected, the leakage mass of the carbon dioxide is obtained within a preset time to obtain the first leakage mass; If the first leakage mass is greater than the target leakage mass, an alarm message is sent and the degree of sealing damage of the wellbore, the degree of sealing damage of the caprock, and the degree of sealing damage of the fault are checked, wherein the carbon dioxide leakage status includes the target leakage mass.

7. An evaluation device for carbon sequestration leakage status, characterized in that, include: The acquisition module is used to acquire injection data corresponding to carbon dioxide, wherein the injection data includes at least one of the following: injection speed, injection pressure, injection location, and injection mass; The first prediction module is used to predict the sealing failure state of the wellbore, the sealing failure state of the caprock, and the sealing failure state of the fault based on the injection data and the target prediction model, wherein the wellbore, the caprock, and the fault are used to seal the carbon dioxide; The second prediction module is used to predict the carbon dioxide leakage status based on the sealing failure status of the wellbore, the sealing failure status of the caprock, and the sealing failure status of the fault.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 6.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method of any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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