Method and device for determining carbon storage in deep land brine and electronic equipment

Abstract

This application provides a method, apparatus, and electronic device for determining carbon sequestration in deep onshore saline aquifers. It includes: acquiring carbon sequestration data; constructing a carbon storage mathematical model based on the carbon sequestration data; a first determination step: determining the component mass fraction in each phase of the carbon storage mathematical model at the current time step based on the carbon sequestration data and flash evaporation equations; a solution processing step: solving the carbon storage mathematical model using the Newton-Krylov algorithm based on field splitting preconditioners based on the component mass fractions to obtain the solution results; a second determination step: determining the step size of the next time step based on the solution results; repeating the first determination step, the solution processing step, and the second determination step at least once until a preset simulation duration is reached to obtain carbon sequestration simulation results; and applying the carbon sequestration simulation results to evaluate the carbon sequestration capacity of deep onshore saline aquifers. This solves the problem of inaccurate evaluation of carbon sequestration capacity in existing technologies for deep onshore saline aquifers.

Description

Technical Field

[0001] This application relates to the technical field of determining carbon sequestration in deep onshore brackish water layers, and more specifically, to a method, apparatus, computer-readable storage medium, and electronic device for determining carbon sequestration in deep onshore brackish water layers. Background Technology

[0002] Geological carbon storage (GCS) has attracted widespread attention as an effective means of reducing atmospheric CO2 concentration. GCS involves injecting industrially derived CO2 into underground aquifers or depleted oil and gas fields for long-term storage to prevent direct emissions of greenhouse gases into the atmosphere. This process requires not only consideration of the physicochemical properties of CO2 but also a deep understanding of the complexity of underground rock formations, including factors such as permeability, porosity, saturation, and temperature. This is crucial for predicting the migration and storage behavior of CO2 in the underground environment.

[0003] Numerical simulation has become an indispensable tool for accurately predicting and optimizing CO2 geological sequestration. Traditional reservoir simulation techniques, such as IMPEC (Implicit Pressure-Explicit Component Alternation) and CPR (Constrained Pressure Residual Method), while widely used in multiphase flow simulations, often face challenges such as time-step limitations, unstable solutions, and low parallel computing efficiency when dealing with complex nonlinear systems involving geological carbon sequestration. Particularly for heterogeneous reservoirs, traditional methods are prone to numerical oscillations, leading to unreliable results and limiting their application in large-scale, long-term simulations. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, computer-readable storage medium, and electronic device for determining carbon sequestration in deep onshore saline aquifers, so as to at least solve the problem that existing technologies are prone to numerical oscillations when dealing with complex nonlinear systems of geological carbon sequestration, resulting in unreliable calculation results and inaccurate assessment of carbon sequestration capacity in deep onshore saline aquifers.

[0005] To achieve the above objectives, according to one aspect of this application, a method for determining carbon sequestration in deep onshore saline aquifers is provided, comprising: an acquisition step: acquiring carbon sequestration data of deep onshore saline aquifers, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is acquired using corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation; a first determination step: determining the mass fraction of components in each phase of the carbon storage mathematical model at the current time step based on the carbon sequestration data and the flash evaporation equation, thereby obtaining the component mass fraction, wherein the components include carbon dioxide, water, and sodium chloride, and the phases include a gas phase and a liquid phase; and a solution processing step: based on the component mass fraction... The carbon storage mathematical model is solved using the Newton-Krylov algorithm based on field splitting preconditioners to obtain the solution results. These results include the carbon storage pressure, carbon dioxide mole fraction, and water mole fraction for the next time step. The carbon storage pressure includes both gas phase pressure and liquid phase pressure. A second determination step involves determining the step size for the next time step based on the solution results. A processing step involves sequentially repeating the first determination step, the solution processing step, and the second determination step at least once until a preset simulation duration is reached to obtain the carbon sequestration simulation results. An evaluation step involves applying the carbon sequestration simulation results to evaluate the carbon sequestration capacity of the deep saline aquifer on land.

[0006] Optionally, the second determining step: determining the step size of the next time step based on the solution result, includes: determining whether the solution result indicates successful solution; if the solution result indicates successful solution, using the first formula: Determine the step size of the next time step, wherein, The step size for the next time step. The step size of the current time step. The first control parameter, This is the preset maximum time step.

[0007] Optionally, after determining whether the solution result indicates a successful solution, the method further includes: if the solution result indicates a failed solution, employing a second formula: Reset the step size of the current time step, where, The step size of the current time step after the reset. To preset the minimum time step, This is the second control parameter.

[0008] Optionally, determining whether the solution result represents a successful solution includes: determining the residual vector of the solution result and determining the norm of the residual vector; obtaining a relative convergence reference and an absolute convergence reference, and determining whether the solution result represents a successful solution based on the norm, the relative convergence reference, and the absolute convergence reference, wherein the solution result is determined to be successful when the norm satisfies at least one of the relative convergence reference and the absolute convergence reference.

[0009] Optionally, determining the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step according to the flash evaporation equation includes: determining the first mole fraction of water in the gas phase using the fugacity coefficient method, and determining the second mole fraction of carbon dioxide in the liquid phase using the activity coefficient method; based on the first mole fraction and the second mole fraction, determining the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step according to the flash evaporation equation.

[0010] Optionally, the Newton-Krylov algorithm based on field splitting preconditioners is used to solve the carbon storage mathematical model to obtain the solution result, including: discretizing the mass conservation equation in the carbon storage mathematical model using the Newton-Krylov algorithm to obtain a discrete mass conservation equation; calculating the inaccurate Newton direction of the discrete mass conservation equation to obtain the search direction, and using line search technology to determine the search step size of the discrete mass conservation equation to obtain the optimal step size; determining the Newton iteration stopping criterion based on the search direction and the optimal step size, and determining the solution result according to the Newton iteration stopping criterion.

[0011] Optionally, after resetting the step size of the current time step, the method further includes: based on the reset step size of the current time step and the component mass fraction, continuing to use the Newton-Krylov algorithm based on the field splitting preconditioner to solve the carbon storage mathematical model.

[0012] According to another aspect of this application, a simulation solution apparatus for carbon sequestration in deep onshore saline aquifers is provided, comprising: an acquisition unit, configured to perform the acquisition step: acquiring carbon sequestration data of deep onshore saline aquifers, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is acquired using corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation; a first determination unit, configured to perform the first determination step: determining the mass fraction of components in each phase of the carbon storage mathematical model at the current time step based on the carbon sequestration data and the flash evaporation equation, thereby obtaining the component mass fraction, wherein the components include carbon dioxide, water, and sodium chloride, and the phases include a gas phase and a liquid phase; and a solution processing unit, configured to perform the solution processing step: based on the component mass fraction, ... The carbon storage mathematical model is solved using the Newton-Krylov algorithm based on field splitting preconditioners to obtain the solution results. These results include the carbon storage pressure, carbon dioxide mole fraction, and water mole fraction for the next time step. The carbon storage pressure includes both gas phase pressure and liquid phase pressure. A second determining unit performs a second determining step: determining the step size of the next time step based on the solution results. An execution unit performs a processing step: sequentially repeating the first determining step, the solution processing step, and the second determining step at least once until a preset simulation duration is reached to obtain the carbon sequestration simulation results. An evaluation unit performs an evaluation step: applying the carbon sequestration simulation results to evaluate the carbon sequestration capacity of the deep saline aquifer on land.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the described methods for determining carbon sequestration in deep onshore saline aquifers.

[0014] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any of the described methods for determining carbon sequestration in deep onshore saline aquifers.

[0015] Applying the technical solution of this application, the process involves the following steps: acquiring carbon sequestration data from deep saline aquifers on land, and constructing a carbon storage mathematical model based on the carbon sequestration data. The carbon sequestration data is collected using corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation. The first determination step involves determining the mass fraction of each component in each phase of the carbon storage mathematical model at the current time step based on the carbon sequestration data and the flash evaporation equation. The components include carbon dioxide, water, and sodium chloride, and the phases include a gas phase and a liquid phase. The solution processing step involves using a field splitting preconditioner based on the component mass fractions. The Newton-Krylov algorithm is used to solve the carbon storage mathematical model, yielding results including carbon storage pressure, carbon dioxide mole fraction, and water mole fraction for the next time step. The carbon storage pressure includes both gas phase and liquid phase pressures. The second determination step determines the step size for the next time step based on the solution results. The processing step involves repeating the first determination step, the processing step, and the second determination step at least once until the preset simulation duration is reached, obtaining the carbon sequestration simulation results. The evaluation step uses the carbon sequestration simulation results to evaluate the carbon sequestration capacity of deep saline aquifers on land. This scheme, combined with the flash evaporation equation, can dynamically calculate the mass fraction of different components in different phases, reflecting the phase changes of components in the actual geological environment. The use of the Newton-Krylov algorithm based on field splitting preconditioners not only accelerates the model solution process and improves computational efficiency but also ensures the stability and accuracy of the simulation through an intelligent step size adjustment mechanism. This invention addresses the problem that existing technologies are prone to numerical oscillations when dealing with complex nonlinear systems of geological carbon sequestration, leading to unreliable calculation results and inaccurate assessments of the carbon sequestration capacity of deep saline aquifers on land. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A hardware block diagram of a mobile terminal for performing a method for determining carbon sequestration in deep onshore saline aquifers, provided in an embodiment of this application, is shown.

[0018] Figure 2 A flowchart illustrating a method for determining carbon sequestration in deep onshore saline aquifers according to an embodiment of this application is shown.

[0019] Figure 3 A schematic diagram of the process for determining an adaptive step size for simulated carbon sequestration is shown according to an embodiment of this application;

[0020] Figure 4A schematic diagram of the process for implicitly solving the mass conservation equation according to an embodiment of this application is shown;

[0021] Figure 5 A structural block diagram of a determination apparatus for carbon sequestration in deep onshore saline aquifers is shown according to an embodiment of this application. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] 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.

[0024] 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 for the embodiments of this application 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.

[0025] As described in the background section, existing technologies are prone to numerical oscillations when dealing with complex nonlinear systems of geological carbon sequestration, leading to unreliable calculation results and inaccurate assessments of the carbon sequestration capacity of deep onshore saline aquifers. To address this problem, embodiments of this application provide a method, apparatus, computer-readable storage medium, and electronic device for determining carbon sequestration in deep onshore saline aquifers.

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] 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 a method of determining carbon sequestration in deep onshore saline aquifers according to an embodiment of the present invention. 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 programmable logic device FPGA, etc.) 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.

[0028] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for determining carbon sequestration in deep saline aquifers in this embodiment of the invention. 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 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 the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] This embodiment provides a method for determining carbon sequestration in deep onshore saline aquifers, which runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] Figure 2 This is a flowchart of a method for determining carbon sequestration in deep onshore saline aquifers according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0031] Step S201, Acquisition Step: Acquire carbon sequestration data of deep saline aquifers on land, and construct a carbon storage mathematical model based on the carbon sequestration data. The carbon sequestration data is acquired by the application of corresponding sensors, and the carbon storage mathematical model includes the mass conservation equation.

[0032] The numerical simulation of carbon dioxide (CO2) sequestration in a saline aquifer involves a multiphase, multi-component system composed of a rock solid framework and pore fluids (CO2, brine, etc.). Its mathematical model mainly includes fundamental governing equations and phase equilibrium calculation methods. The fundamental governing equations include mass conservation equations, energy conservation equations, stress balance equations, and chemical reactions. Here, it is assumed that only the mass conservation equation is governing, the phases are gas and liquid, and the components are carbon dioxide, water, and sodium chloride (NaCl). The mass conservation equation is a fundamental physical law describing the constant total amount of matter within the system, ensuring the mass conservation of carbon dioxide, water, and sodium chloride in the gas and liquid phases during the simulation.

[0033] Step S202, First determination step: Based on the carbon sequestration data and flash evaporation equation, determine the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step, and obtain the component mass fraction, wherein the components include carbon dioxide, water and sodium chloride, and the phases include gas phase and liquid phase;

[0034] In step S202 above, combined with the flash evaporation equation, this scheme can dynamically calculate the mass fraction of different components in different phase states, reflecting the phase changes of components in the actual geological environment.

[0035] Step S203, Solution Processing Step: Based on the above component mass fractions, the Newton-Krylov algorithm based on field splitting preconditioners is used to solve the above carbon storage mathematical model to obtain the solution results. The solution results include the carbon storage pressure, carbon dioxide mole fraction, and water mole fraction at the next time step. The carbon storage pressure includes gas phase pressure and liquid phase pressure.

[0036] Specifically, the Newton-Krylov algorithm based on field splitting preconditioners not only accelerates the model solution process and improves computational efficiency, but also addresses the problems of low computational efficiency and poor simulation accuracy in traditional simulation methods. This provides a scientific basis for assessing the carbon sequestration potential of saline aquifers and helps optimize the site selection and design of carbon capture and storage (CCS) projects. In the technical field, this method is not only applicable to carbon dioxide sequestration but can also be extended to geological sequestration research of other greenhouse gases, including but not limited to methane and nitrous oxide.

[0037] Step S204, Second determination step: Determine the step size of the next time step based on the above solution results;

[0038] Specifically, the solution is determined based on the solution results to determine whether the solution is successful, and the step size of the next time step is set accordingly. Step S204 ensures the stability and accuracy of the simulation through an intelligent step size adjustment mechanism.

[0039] Step S205, Processing steps: Repeat the above first determination step, the above solution processing step and the above second determination step at least once in sequence until the preset simulation time is reached to obtain the carbon sequestration simulation results;

[0040] The preset simulation duration can be set according to the simulation requirements. When the total simulation duration reaches the preset simulation duration, the carbon sequestration simulation of the deep saline aquifer on land is completed, and the carbon sequestration simulation results under the preset simulation duration are obtained.

[0041] Step S206, Evaluation Step: Apply the above carbon sequestration simulation results to evaluate the carbon sequestration capacity of the above-mentioned deep saline aquifer on land.

[0042] By employing the Newton-Krylov adaptive time algorithm based on field splitting (FS) preconditioners, this method inherits the advantage of fully implicit solution methods that are unrestricted by time steps and improves the algorithm's stability. Compared to existing techniques, this method offers greater flexibility in time step selection. Combining the standard solution strategy in reservoir simulation, the Newton-Rapson method, with Krylov subspace techniques enhances the robustness and efficiency of the iterative method.

[0043] In this embodiment, by applying steps S201, S202, S203, S204, S205, and S206, the combined advantage of the FS preconditioner and the Newton-Krylov method lies in its integration of multiple solution methods, fully leveraging their respective strengths. This allows for efficient solution of complex nonlinear systems and possesses good scalability and parallel computing capabilities. Combined with the flash evaporation equation, this scheme can dynamically calculate the mass fractions of different components in different phase states, reflecting the phase changes of components in actual geological environments. The Newton-Krylov algorithm based on the field splitting preconditioner not only accelerates the model solution process and improves computational efficiency but also ensures the stability and accuracy of the simulation through an intelligent step-size adjustment mechanism. This solves the problem that existing technologies, when dealing with complex nonlinear systems of geological carbon sequestration, are prone to numerical oscillations, leading to unreliable calculation results and inaccurate assessments of the carbon sequestration capacity of deep saline aquifers on land.

[0044] In the specific implementation process, the second determining step is: determining the step size of the next time step based on the above solution results, including: determining whether the above solution results indicate successful solution; if the above solution results indicate successful solution, the first formula is used: Determine the step size for the next time step, where, The step size for the next time step is as described above. The step size is the current time step mentioned above. The first control parameter, This is the preset maximum time step.

[0045] In this technical solution, the convergence of the simulation process can be determined by checking the norm of the residual vector of the solution results, thus confirming the success of the solution. If the solution is successful, the step size of the next time step will be dynamically adjusted based on the current time step size, the first control parameter, and the preset maximum time step size to optimize simulation efficiency. This method, by intelligently adjusting the time step size, avoids numerical instability caused by an excessively large step size or wasted computational resources caused by an excessively small step size during the simulation process, achieving high efficiency and stability in the simulation process. In practical applications, the first control parameter can be adjusted according to the specific needs of the simulation and geological conditions to achieve the best simulation results.

[0046] Specifically, after determining whether the above solution result indicates a successful solution, the method further includes: if the above solution result indicates a failed solution, using the second formula: Reset the step size of the current time step, where, The step size of the current time step after the reset. To preset the minimum time step, This is the second control parameter.

[0047] During the simulation, if the solution results indicate that the simulation has not converged (i.e., the solution has failed), this technical solution resets the step size of the current time step using a second formula to ensure the stability of the simulation process. The preset minimum time step size and the second control parameter provide a safety net for the simulation, preventing simulation failure due to improper step size settings. This dynamic adjustment mechanism not only improves the robustness of the simulation but also ensures the accuracy and reliability of the model under complex geological conditions. For example, when encountering abrupt changes in geological structure or drastic changes in injection conditions, reducing the time step size allows for more precise capture of these changes, thereby improving the reliability of the simulation results.

[0048] Further, determining whether the above solution result represents a successful solution includes: determining the residual vector of the above solution result and determining the norm of the above residual vector; obtaining a relative convergence reference and an absolute convergence reference, and determining whether the above solution result represents a successful solution based on the above norm, the above relative convergence reference, and the above absolute convergence reference, wherein the above solution result is determined to be successful when the above norm satisfies at least one of the above relative convergence reference and the above absolute convergence reference.

[0049] This technical solution calculates the norm of the residual vector in the solution and compares it with preset relative and absolute convergence references to determine whether the simulation has met the convergence criteria, i.e., whether the solution is successful. The norm of the residual vector reflects the degree of deviation between the model's solution and the actual physical process, and is a key indicator for evaluating simulation accuracy. The setting of relative and absolute convergence references provides a set of quantitative standards for the simulation, ensuring the accuracy and reliability of the simulation results. This method solves the problem of unclear convergence judgment criteria in traditional simulations, improving the scientific rigor and effectiveness of the simulation.

[0050] Furthermore, based on the flash evaporation equation, the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step is determined, including: determining the first mole fraction of water in the gas phase using the fugacity coefficient method, and determining the second mole fraction of carbon dioxide in the liquid phase using the activity coefficient method; based on the first mole fraction and the second mole fraction, the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step is determined again according to the flash evaporation equation.

[0051] This technical solution accurately calculates the mass fraction of carbon dioxide in the gas phase and liquid phase using the fugacity coefficient method and the activity coefficient method, combined with the flash evaporation equation. This is a crucial step in simulating the carbon dioxide sequestration process in saline aquifers. The fugacity coefficient method and the activity coefficient method are used to describe the non-ideal behavior of components in the gas and liquid phases, respectively. These methods can more accurately reflect the phase changes and interactions of components in actual geological environments. The flash evaporation equation is used to balance the component distribution between different phases, ensuring the mass conservation of components between the gas and liquid phases during the simulation. This method solves the problem of inaccurate description of component phase changes in traditional simulations, improving the physical reliability of the simulation results. Furthermore, in the field of geological engineering, this method can be applied to a wider range of multi-component, multi-phase flow simulations, including but not limited to oil and gas reservoir development and geothermal resource utilization. By accurately calculating the mass fraction of different components in different phases, the flow characteristics of underground fluids can be more scientifically evaluated, providing accurate data support for the planning and design of geological engineering projects.

[0052] Specifically, the Newton-Krylov algorithm based on field splitting preconditioners is used to solve the above-mentioned carbon storage mathematical model to obtain the solution results. This includes: discretizing the above-mentioned mass conservation equation in the above-mentioned carbon storage mathematical model using the Newton-Krylov algorithm to obtain discrete mass conservation equations; calculating the inaccurate Newton direction of the above-mentioned discrete mass conservation equations to obtain the search direction, and using line search technology to determine the search step size of the above-mentioned discrete mass conservation equations to obtain the optimal step size; determining the Newton iteration stopping criterion based on the above-mentioned search direction and the above-mentioned optimal step size, and determining the above-mentioned solution results according to the above-mentioned Newton iteration stopping criterion.

[0053] This technical solution utilizes the efficient solution strategy of the Newton-Krylov algorithm to achieve accurate discretization and solution of the mass conservation equation in a carbon storage mathematical model. The Newton-Krylov algorithm combines the local convergence of Newton's method with the global search capability of the Krylov subspace method. By calculating the inaccurate Newton direction and employing a line search technique to determine the optimal step size, it can quickly find solutions that satisfy Newton's iteration stopping criteria, thereby improving the computational efficiency and accuracy of the simulation. This method solves the problems of slow solution speed and low accuracy in traditional simulations, providing an efficient and accurate computational tool for large-scale geological carbon sequestration research. In the field of geological engineering, the Newton-Krylov algorithm has a wide range of applications, including but not limited to geomechanical simulation and groundwater resource management. By optimizing the solution algorithm, the efficiency of simulation can be significantly improved, providing timely and reliable data support for geological engineering decision-making. The flexibility and efficiency of this method give it a significant advantage in handling complex geological problems.

[0054] More specifically, after resetting the step size of the current time step, the method further includes: based on the reset step size of the current time step and the mass fraction of the components, continuing to use the Newton-Krylov algorithm based on the field splitting preconditioner to solve the carbon storage mathematical model.

[0055] When encountering solution failures, this technical solution resets the time step to ensure the stability and accuracy of the simulation process. After resetting the time step, the Newton-Krylov algorithm based on field splitting preconditioners is continued for solution processing, which can fully utilize the algorithm's efficient solution capability and quickly restore the simulation's convergence. This method solves the problem of the lack of effective strategies in traditional simulations when encountering numerical instability, improving the robustness and practicality of the simulation.

[0056] Furthermore, this solution also incorporates the application of dynamic mesh adaptive technology. This technology enhances the model's simulation accuracy and computational efficiency for complex geological structures in saline aquifers. Dynamic mesh adaptive technology dynamically adjusts the mesh resolution based on changes in physical fields (such as pressure and saturation). Specifically, it automatically increases mesh density in regions of high change rate and decreases mesh density in regions of gradual change. This significantly reduces computational costs and resource consumption while maintaining model accuracy. Specific implementation examples are as follows:

[0057] In practical simulations of carbon sequestration in saline aquifers, an initial grid is first established based on initial geological parameters (such as permeability and porosity). During each iterative solution step, a dynamic grid adaptation technique is used to evaluate the rate of change of physical quantities in each grid cell, based on the solution obtained from the Newton-Krylov algorithm with FS preconditioners. Regions with high rates of change (such as those near injection wells or high-permeability areas) are refined, increasing the number of grid cells, while regions with low rates of change (such as stable saline aquifers far from active areas) are coarsened, reducing the number of grid cells. This dynamic adjustment ensures high-accuracy solutions in highly dynamic regions with injected CO2, while maintaining computational efficiency in relatively stable regions.

[0058] This scheme also includes a machine learning-based preprocessing method for predicting and optimizing the selection of the initial time step and precondition sub-parameters, thereby accelerating the convergence speed of the Newton-Krylov algorithm and reducing the number of iterations. This method analyzes historical simulation data to train a machine learning model to predict the most suitable initial conditions and parameters for rapid convergence. A specific implementation example is as follows:

[0059] During the development phase, a large amount of case data from saline aquifer carbon sequestration simulations was collected, including simulation results under different geological features, injection conditions, and physical parameters. Based on this data, a regression model was trained that can predict an initial time step and optimal preconditioner parameter settings (such as the values ​​of α and β) based on the input geological parameters and physical conditions for use in the Newton-Krylov algorithm for the FS preconditioner. At the start of the simulation, the model automatically adjusts the time step and preconditioner parameters according to the current geological conditions, avoiding traditional trial-and-error methods.

[0060] In simulation tests, compared with traditional fixed parameter settings, using machine learning-based preprocessing methods can reduce the number of iterations of the Newton-Krylov algorithm by an average of about 40%, significantly accelerating the solution speed. Furthermore, this method improves the stability of the simulation and reduces convergence issues, especially under complex conditions in heterogeneous reservoirs, where the effect is particularly significant.

[0061] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the method for determining carbon sequestration in deep onshore saline aquifers will be described in detail below with reference to specific embodiments.

[0062] This embodiment relates to a specific method for determining carbon sequestration in deep onshore saline aquifers. It employs the Newton-Krylov adaptive time algorithm based on field splitting (FS) preconditioners to achieve large-scale parallel solution to this problem. The following is a detailed description of this embodiment:

[0063] Numerical simulations of CO2 sequestration in saline aquifers involve multiphase, multicomponent systems composed of a solid rock framework and pore fluids (CO2, brine, etc.). The mathematical model primarily includes fundamental governing equations and phase equilibrium calculation methods. The fundamental governing equations include mass conservation equations, energy conservation equations, stress equilibrium equations, and chemical reaction equations. Here, it is assumed that the governing equations are only mass and energy conservation equations, the phases are gas and liquid, and the components are carbon dioxide, water, and NaCl.

[0064] The mass conservation equation is:

[0065] ;

[0066] in, Porosity is the ratio of the pore volume to the surface volume of a rock. α is the saturation of the α phase (gas or liquid phase, represented by the subscript g or l), used to describe the degree to which the pores of the reservoir rock are filled with fluid; The density of the α phase; The mass fraction of component K (carbon dioxide, water, or NaCl) in the α phase refers to the percentage of a certain substance in the total mass of the mixture; It is the external source phase or sink phase of component K (i.e., there is injection or production).

[0067] For the flux of fluid component K:

[0068] ;

[0069] Where k is the absolute penetration rate, The relative permeability of the α phase is generally between 0 and 1. The viscosity of the α phase is a physical quantity that measures the magnitude of fluid viscosity. Let g be the pressure of phase α, and g be the acceleration due to gravity. For diffusion and dispersion tensors, i.e., the diffusion of matter due to concentration gradients.

[0070] Vapor pressure With liquid phase pressure The pressure difference is the capillary pressure ( (Generally, the data is experimental data, obtained using interpolation methods);

[0071] in = - , , This represents the mass fraction of NaCl. This represents the molecular weight of NaCl.

[0072] water saturation Gas saturation The sum of is 1. for The mass fraction of the k-th component in the phase, and the sum of the mass fractions of all components is 1:

[0073] ;

[0074] ;

[0075] Where w represents the aqueous phase, g represents the gas phase, n is the number of phases, and k is the number of components.

[0076] The activity coefficient method is used to represent the exchange of components between the gas and liquid phases, where the fugacity coefficient method is used to calculate the gas phase, as shown in the following formula:

[0077] ;

[0078] in, Let m be the fugacity coefficient of component m in phase α. The mole fraction of component l in phase α. For the empirical factor of component l, the subscript is... The subscript m represents the component carbon dioxide or water. , For the empirical factor of pure component m, , for The empirical factors of the phase, It is a binary interaction parameter between components, where Nc is the number of components. The compression factor is 1. and These represent the dimensionless quantities of the attractive and repulsive forces between molecules, respectively.

[0079] The activity coefficient is used to calculate the liquid phase, and the formula is as follows:

[0080] Activity coefficient ;

[0081] in This represents the molar concentration of NaCl. ;

[0082] coefficient ;

[0083] coefficient ;

[0084] Where T is temperature, in Kelvin (from 273 K to 533 K), and P is pressure, in bar (from 0 to 2000 bar).

[0085] The mole fractions of Co2 in the liquid phase and H2O in the gas phase can be calculated using the fugacity and activity methods described above. Then, the mass fraction V of H2O in the gas phase can be calculated using the flash evaporation equation, which is:

[0086] ;

[0087] Where V is the mass fraction of the fluid in the gas phase, and is an unknown quantity; It is the total mass fraction of component i; It is the equilibrium constant. ,in, for Phase saturation, for Phase density, for The mass fraction of the i-th component in the phase, Mass fraction of fluid component i in the gas phase. Let i be the mass fraction of component i in the liquid phase.

[0088] The aforementioned nonlinear coupled partial differential equation system describes the multiphase, multicomponent problem in carbon sequestration. This equation system encompasses the mass conservation equations, momentum equations, and phase equilibrium equations for each component, characterizing the flow, diffusion, interaction, and transformation processes of each component in the saline aquifer.

[0089] Adaptive time step setting, such as Figure 3 As shown, it includes the following:

[0090] Preset minimum time step t min With the maximum time step t max And the control parameters α and β. If the solution is successful at the current time, the next time step will be min(t). max α × previous time step size); if the solution fails at the current time, reset the time step size to max(t). min , β×the previous time step), where 1=<α<=2, 0<β<=1.

[0091] This embodiment implements a Newton-Krylov fully implicit adaptive time algorithm based on the field splitting (FS) preconditioner. This embodiment automatically adjusts the time step, solving the problem of non-convergence due to excessively large time steps, and also overcomes the load imbalance problem. Figure 4 As shown, it specifically includes the following:

[0092] Initialize solution vector: Set the initial solution vector;

[0093] Calculate the mass fraction of the component in each phase based on the flash evaporation equation;

[0094] The conservation equations are solved using the Newton-Krylov algorithm: The conservation equations are solved using the Newton-Krylov algorithm based on field splitting (FS) preconditioners, and the solution vector is updated for the next time step;

[0095] Update time step: Use an adaptive time step method to set the time step for each step until the final simulation time is reached.

[0096] Through the above, this embodiment can effectively solve the component equations, give full play to their respective advantages, and obtain stable and accurate results.

[0097] Furthermore, the Newton-Krylov algorithm is a popular parallel solution method for addressing the challenges of solving large-scale nonlinear equation systems. This algorithm integrates Newton's method and the Krylov subspace method. The detailed steps of the algorithm are described below:

[0098] First, the nonlinear partial differential equation is discretized using the Newton-Krylov method. Let represent the initial solution vector, then the next approximate solution of the Newton-Krylov algorithm... The update is as follows, and the specific steps are as follows:

[0099] Step S1: (Initialization) Give initial values And set the number of steps k=0;

[0100] Step S2: (Determine the search direction) Calculate the non-precise Newtonian direction. , so that:

[0101] ,in For Jacobian matrices, As a preconditioner, For the inverse of the preconditioner, , These are relative error and absolute error, respectively. This represents the nonlinear equations of the entire model.

[0102] Step S3: (Determine the search step size) Use line search technology to find the optimal step size. ,in, The parameter α is a constant greater than 0.

[0103] Step S4: (Stopping condition) Update the approximate solution Determine the stopping criterion for Newton's iterations: Otherwise, set k=k+1 and execute step S2; ε r and ε a These represent the relative and absolute errors of Newton's iteration, respectively.

[0104] In the Newton-Krylov method, one of the most important components of the solver is selecting appropriate preconditioners, because linearized systems are often difficult to solve. Therefore, in step S2... It is a FS preconditioner, which can better solve linear equations.

[0105] FS preconditioners are solved using relaxation or factorization. For simplicity, the matrix in a linear block system is restricted to... The blocks (which can generally be handled in a similar way) are represented by the Jacobi matrix as follows:

[0106]

[0107] The following introduces a special type of FS preconditioner, also called the CPR preconditioner. The CPR method is a two-stage piecewise process that solves a smaller, simplified system by eliminating variables, and then approximates the original complete system. Let... and These are the approximate inverse actions of the system. and The matrix, the first-stage preconditioner for:

[0108]

[0109] The second stage preconditioner approximates the solution of the original complete system, so it is approximated by the inverse of the Jacob matrix, i.e. ;

[0110] In summary, for For linear systems, the steps of the CPR method are as follows:

[0111] use Preconditioner: ;

[0112] Calculate the new residual: ;

[0113] use Preconditioner and correction: ;

[0114] In this case, the CPR preconditioner can be written as:

[0115]

[0116] in, and The Schwarz preconditioner is a domain-based decomposition technique that divides the solution domain into multiple subdomains and constructs independent preconditioners for each subdomain. By fully utilizing the structural characteristics and local information of the linear equation system, it can effectively accelerate linear convergence and reduce the computational burden of the overall iteration. Therefore, a restricted Schwarz preconditioner expression is as follows:

[0117] , Where is the number of processors, and T is the transpose sign. subfield Jacobian matrix The approximate inverse, These are the constraint operator and the interpolation operator, respectively;

[0118] When solving linear systems of equations, the GMRES method finds the linear combination of minimum residual vectors by establishing a Krylov subspace in each iteration and progressively approximating the exact solution. Given the GMRES method's wide applicability, good iterative convergence, and efficient memory utilization, we choose this method to solve linear systems.

[0119] In summary, the combined advantage of the FS preconditioner and the Newton-Krylov method lies in its combination of multiple solution methods, which fully leverages the strengths of each method, enabling efficient solutions to complex nonlinear systems, and possessing good scalability and parallel computing capabilities.

[0120] This embodiment employs the Newton-Krylov adaptive time algorithm based on the field splitting (FS) preconditioner. This approach inherits the advantage of fully implicit solution methods, which are unrestricted by time step size, and improves the algorithm's stability. Compared to existing technologies, this approach offers greater flexibility in time step selection. Combining the standard solution strategies in reservoir simulation, the Newton-Rapson method and Krylov subspace technique, enhances the robustness and efficiency of the iterative method. Furthermore, the Schwarz preprocessing technique is applied in the preprocessing stage of linear iteration to accelerate the algorithm's convergence speed. In summary, this embodiment can be effectively applied to large-scale parallel simulation methods for multiphase, multicomponent seepage problems.

[0121] In a practical application scenario, reservoir numerical simulation was performed using the scheme provided in this embodiment. The fully implicit solution method proposed in this invention was implemented on a computer based on the PETSc toolkit. After verification through a saline water layer sealing test experiment, the results showed that the mass fraction of carbon dioxide in the saline water completely matched the reference solution calculated in MOOSE, and the numerical results were stable. This method supports multi-core computation and is suitable for large-scale parallel solution of multiphase and multicomponent problems in reservoir simulation.

[0122] This application also provides an apparatus for determining carbon sequestration in deep onshore brackish water aquifers. It should be noted that this apparatus can be used to execute the method for determining carbon sequestration in deep onshore brackish water aquifers provided in this application. This apparatus is used to implement the above embodiments and preferred embodiments; 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 apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0123] The following describes the apparatus for determining carbon sequestration in deep saline aquifers on land, as provided in the embodiments of this application.

[0124] Figure 5 This is a schematic diagram of a determination apparatus for carbon sequestration in deep onshore saline aquifers according to an embodiment of this application. Figure 5 As shown, the device includes:

[0125] Acquisition unit 51 is used to perform the acquisition steps: acquire carbon sequestration data of deep saline aquifers on land, and construct a carbon storage mathematical model based on the carbon sequestration data. The carbon sequestration data is acquired by the application of corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation.

[0126] The first determining unit 52 is used to perform the first determining step: based on the carbon sequestration data and flash evaporation equation, determine the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step, and obtain the component mass fraction, wherein the components include carbon dioxide, water and sodium chloride, and the phases include gas phase and liquid phase;

[0127] The solution processing unit 53 is used to perform the solution processing steps: based on the above component mass fractions, the Newton-Krylov algorithm based on field splitting preconditioners is used to solve the above carbon storage mathematical model to obtain the solution results, wherein the above solution results include the carbon storage pressure, carbon dioxide mole fraction and water mole fraction of the next time step, and the above carbon storage pressure includes gas phase pressure and liquid phase pressure.

[0128] The second determining unit 54 is used to perform the second determining step: determining the step size of the next time step based on the above solution result;

[0129] Execution unit 55 is used to execute the following processing steps: repeat the first determination step, the solution processing step and the second determination step at least once in sequence until the preset simulation time is reached to obtain the carbon sequestration simulation result;

[0130] Evaluation unit 56 is used to perform the evaluation steps: applying the above carbon sequestration simulation results to evaluate the carbon sequestration capacity of the above-mentioned deep saline aquifer on land.

[0131] In this embodiment, an acquisition unit is used to perform the acquisition steps: acquiring carbon sequestration data of deep saline aquifers on land, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is collected by corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation; a first determination unit is used to perform the first determination step: determining the mass fraction of each component in each phase of the carbon storage mathematical model at the current time step according to the carbon sequestration data and the flash evaporation equation, obtaining the component mass fraction, wherein the components include carbon dioxide, water, and sodium chloride, and the phases include a gas phase and a liquid phase; a solution processing unit is used to perform the solution processing step: based on the component mass fraction, using a Ne model based on field splitting preconditioners. The Wton-Krylov algorithm solves the carbon storage mathematical model to obtain the solution results, which include the carbon storage pressure, carbon dioxide mole fraction, and water mole fraction for the next time step. The carbon storage pressure includes both gas phase pressure and liquid phase pressure. A second determining unit is used to execute the second determining step: determining the step size for the next time step based on the solution results. An execution unit is used to execute the processing step: repeatedly executing the first determining step, the solution processing step, and the second determining step at least once until a preset simulation duration is reached to obtain the carbon sequestration simulation results. An evaluation unit is used to execute the evaluation step: evaluating the carbon sequestration capacity of deep saline aquifers on land using the carbon sequestration simulation results.

[0132] As an optional solution, the second determining unit includes a first determining module and a second determining module; the first determining module is used to determine whether the above solution result indicates a successful solution; the second determining module is used to apply the first formula if the above solution result indicates a successful solution: Determine the step size for the next time step, where, The step size for the next time step is as described above. The step size is the current time step mentioned above. The first control parameter, This is the preset maximum time step.

[0133] In an optional embodiment, the second determining unit further includes a reset module, used to employ a second formula if the above-mentioned solution result indicates a solution failure: Reset the step size of the current time step, where, The step size of the current time step after the reset. To preset the minimum time step, This is the second control parameter.

[0134] In one optional scheme, the first determining module includes a first determining submodule and a first obtaining submodule; the first determining submodule is used to determine the residual vector of the above solution result and determine the norm of the above residual vector; the first obtaining submodule is used to obtain a relative convergence reference and an absolute convergence reference, and determine whether the above solution result represents a successful solution based on the above norm, the above relative convergence reference and the above absolute convergence reference, wherein the above solution result is determined to be successful when the above norm satisfies at least one of the above relative convergence reference and the above absolute convergence reference.

[0135] In one optional scheme, the first determining unit includes a third determining module and a fourth determining module; the third determining module is used to determine the first mole fraction of water in the gas phase using the fugacity coefficient method, and to determine the second mole fraction of carbon dioxide in the liquid phase using the activity coefficient method; the fourth determining module is used to determine the mass fraction of the components in each phase in the carbon storage mathematical model at the current time step based on the first mole fraction and the second mole fraction, and according to the flash evaporation equation.

[0136] An optional scheme is provided, wherein the solution processing unit includes a discretization module, a first calculation module, and a fifth determination module; the discretization module is used to discretize the mass conservation equation in the above-mentioned carbon storage mathematical model using the Newton-Krylov algorithm to obtain a discrete mass conservation equation; the first calculation module is used to calculate the inaccurate Newton direction of the above-mentioned discrete mass conservation equation to obtain the search direction, and uses a line search technique to determine the search step size of the above-mentioned discrete mass conservation equation to obtain the optimal step size; the fifth determination module is used to determine the Newton iteration stopping criterion based on the above-mentioned search direction and the above-mentioned optimal step size, and determine the above-mentioned solution result according to the above-mentioned Newton iteration stopping criterion.

[0137] In one alternative, the second determining unit further includes a solution processing module, which, after resetting the step size of the current time step, continues to use the Newton-Krylov algorithm based on the field splitting preconditioner to solve the carbon storage mathematical model based on the reset step size of the current time step and the component mass fraction.

[0138] The aforementioned device for determining carbon sequestration in deep onshore saline aquifers includes a processor and a memory. The acquisition unit, first determination unit, solution processing unit, second determination unit, execution unit, and evaluation unit are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve their respective functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0139] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting the kernel parameters, the problem that existing technologies are prone to numerical oscillations when dealing with complex nonlinear systems of geological carbon sequestration, leading to unreliable calculation results and inaccurate assessments of the carbon sequestration capacity of deep onshore saline aquifers can be addressed.

[0140] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0141] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining carbon sequestration in deep onshore saline aquifers.

[0142] This invention provides a processor for running a program, wherein the program executes the method for determining carbon sequestration in deep onshore saline aquifers.

[0143] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the steps of the above-described method for determining carbon sequestration in deep saline aquifers on land.

[0144] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0145] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform a program that initializes a method for determining carbon sequestration in deep onshore saline aquifers, having at least the steps described above.

[0146] It is obvious to those skilled in the art that the modules or steps of the present invention 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 described herein, 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, the present invention is not limited to any particular combination of hardware and software.

[0147] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0152] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0153] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0154] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0155] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining carbon sequestration in deep onshore saline aquifers, characterized in that, include: Acquisition steps: Acquire carbon sequestration data from deep saline aquifers on land, and construct a carbon storage mathematical model based on the carbon sequestration data. The carbon sequestration data is collected using corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation. First determination step: Based on the carbon sequestration data and flash evaporation equation, determine the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step, and obtain the component mass fraction, wherein the components include carbon dioxide, water and sodium chloride, and the phases include gas phase and liquid phase; Solution processing steps: Based on the mass fraction of the components, the Newton-Krylov algorithm based on field splitting preconditioners is used to solve the carbon storage mathematical model to obtain the solution results, wherein the solution results include the carbon storage pressure, carbon dioxide mole fraction and water mole fraction at the next time step, and the carbon storage pressure includes gas phase pressure and liquid phase pressure; The second determination step: Determine the step size of the next time step based on the solution result; Processing steps: Repeat the first determining step, the solving process step, and the second determining step at least once in sequence until the preset simulation time is reached to obtain the carbon sequestration simulation results; Evaluation steps: The carbon sequestration simulation results are used to evaluate the carbon sequestration capacity of the deep saline aquifer on land.

2. The method according to claim 1, characterized in that, The second determination step: Based on the solution result, determine the step size of the next time step, including: Determine whether the solution result indicates that the solution was successful; If the solution result indicates a successful solution, the first formula shall be used: Determine the step size of the next time step, wherein, The step size for the next time step. The step size of the current time step. The first control parameter, This is the preset maximum time step.

3. The method according to claim 2, characterized in that, After determining whether the solution result indicates a successful solution, the method further includes: In the event that the solution result indicates a failure, the second formula is used: Reset the step size of the current time step, where, The step size of the current time step after the reset. To preset the minimum time step, This is the second control parameter.

4. The method according to claim 2, characterized in that, Determining whether the solution result indicates a successful solution includes: Determine the residual vector of the solution result, and determine the norm of the residual vector; Obtain a relative convergence reference and an absolute convergence reference, and determine whether the solution result represents a successful solution based on the norm, the relative convergence reference, and the absolute convergence reference. The solution result is determined to be successful when the norm satisfies at least one of the relative convergence reference and the absolute convergence reference.

5. The method according to claim 1, characterized in that, Based on the flash evaporation equation, determine the mass fraction of each component in each phase in the carbon storage mathematical model at the current time step, including: The first mole fraction of water in the gas phase is determined using the fugacity coefficient method, and the second mole fraction of carbon dioxide in the liquid phase is determined using the activity coefficient method. Based on the first mole fraction and the second mole fraction, and according to the flash evaporation equation, the mass fraction of the component in each phase in the carbon storage mathematical model at the current time step is determined.

6. The method according to claim 1, characterized in that, The Newton-Krylov algorithm based on field splitting preconditioners is used to solve the carbon storage mathematical model, and the solution results are obtained, including: The mass conservation equation in the carbon storage mathematical model is discretized using the Newton-Krylov algorithm to obtain the discrete mass conservation equation; The inaccurate Newton direction of the discrete mass conservation equation is calculated to obtain the search direction, and the search step size of the discrete mass conservation equation is determined by the line search technique to obtain the optimal step size; Based on the search direction and the optimal step size, the Newton iteration stopping criterion is determined, and the solution result is determined according to the Newton iteration stopping criterion.

7. The method according to claim 3, characterized in that, After resetting the step size of the current time step, the method further includes: Based on the reset step size of the current time step and the component mass fraction, the Newton-Krylov algorithm based on the field splitting preconditioner is continued to solve the carbon storage mathematical model.

8. A simulation and solution apparatus for carbon sequestration in deep onshore saline aquifers, characterized in that, include: The acquisition unit is used to perform the acquisition steps: acquiring carbon sequestration data of deep saline aquifers on land, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is acquired by applying corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation. The first determining unit is used to perform the first determining step: based on the carbon sequestration data and the flash evaporation equation, determine the mass fraction of the components in each phase of the carbon storage mathematical model at the current time step, and obtain the component mass fraction, wherein the components include carbon dioxide, water and sodium chloride, and the phase includes a gas phase and a liquid phase; The solution processing unit is used to perform the solution processing steps: based on the component mass fraction, the Newton-Krylov algorithm based on the field splitting preconditioner is used to solve the carbon storage mathematical model to obtain the solution results, wherein the solution results include the carbon storage pressure, carbon dioxide mole fraction and water mole fraction at the next time step, and the carbon storage pressure includes the gas phase pressure and the liquid phase pressure. The second determining unit is used to perform the second determining step: determining the step size of the next time step based on the solution result; An execution unit is used to execute the following processing steps: the first determining step, the solution processing step, and the second determining step are executed at least once in sequence until a preset simulation duration is reached to obtain carbon sequestration simulation results; An evaluation unit is used to perform the evaluation step of applying the carbon sequestration simulation results to evaluate the carbon sequestration capacity of the deep saline aquifer on land.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining carbon sequestration in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for performing the determination method for carbon sequestration in deep onshore saline aquifers as described in any one of claims 1 to 7.

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