Method and device for determining carbon sequestration of land deep saline water layer and electronic equipment

Abstract

The invention provides a land deep saline water layer-oriented carbon sequestration determination method and apparatus, and an electronic device. Comprising the steps of obtaining carbon sequestration data, and constructing a carbon storage mathematical model based on the carbon sequestration data; a first determination step: determining the component mass fraction of the components in each phase in the carbon storage mathematical model of the current time step according to the carbon sequestration data and a flash evaporation equation; a solving processing step: based on the component mass fraction, solving the carbon storage mathematical model by adopting a Newton-krylov algorithm based on a field splitting precondition to obtain a solving result; a second determination step: determining the step length of the next time step according to the solving result; the first determining step, the solving processing step and the second determining step are repeatedly executed at least once until the preset simulation duration is reached, and a carbon sequestration simulation result is obtained; and evaluating the carbon sequestration capability of the land deep saline water layer by using a carbon sequestration simulation result. The problem that the carbon sequestration capacity of the land deep saline water layer is not accurately evaluated in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the technical field of determining carbon sequestration in deep onshore saline aquifers, and specifically to a method, device, computer-readable storage medium, and electronic device for determining carbon sequestration in deep onshore saline aquifers. Background Art

[0002] Geological carbon storage (GCS) has attracted widespread attention as an effective means of reducing atmospheric CO2 concentrations. GCS involves injecting industrially derived CO2 into underground saline aquifers or depleted oil and gas fields for long-term storage, thereby preventing the direct release of greenhouse gases into the atmosphere. This process requires not only consideration of the physical and chemical 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 underground environments.

[0003] Numerical simulation has become an indispensable tool for accurately predicting and optimizing the geological storage of CO2. Traditional reservoir simulation techniques, such as IMPEC (Implicit Pressure-Explicit Composition Alternation) and CPR (Constrained Pressure Residual), have been widely used in multiphase flow simulation. However, when dealing with the complex nonlinear systems of geological carbon storage, these methods often face challenges such as limited time steps, unstable solutions, and inefficient parallel computing. Especially for heterogeneous subsurface reservoirs, traditional methods are prone to numerical oscillations, resulting in unreliable results and limiting their application in large-scale, long-timescale simulations. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, 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 the existing technology is prone to numerical oscillation when dealing with complex nonlinear systems of geological carbon sequestration, resulting in unreliable calculation results and inaccurate assessment of the carbon sequestration capacity of deep onshore saline aquifers.

[0005] To achieve the above-mentioned purpose, according to one aspect of the present 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 collected by applying 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 in the carbon storage mathematical model at the current time step based on the carbon sequestration data and the flash evaporation equation, and 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 step: determining the mass fraction of the components in each phase based on the carbon sequestration data and the flash evaporation equation, and obtaining the component mass fraction. number, using the Newton-Krylov algorithm based on the field splitting preconditioner to solve the carbon storage mathematical model to obtain a solution result, wherein the solution result includes the carbon storage pressure, carbon dioxide mole fraction and water mole fraction in the next time step, and the carbon storage pressure includes gas phase pressure and liquid phase pressure; a second determining step: determining the step size of the next time step according to the solution result; a processing step: repeating the first determining step, the solution processing step and the second determining step in sequence at least once until the preset simulation time is reached to obtain a carbon sequestration simulation result; an evaluation step: applying the carbon sequestration simulation result to evaluate the carbon sequestration capacity of the onshore deep saline layer.

[0006] Optionally, the second determining step: determining the step size of the next time step according to the solution result includes: determining whether the solution result indicates a successful solution; if the solution result indicates a successful solution, using the first formula: , determine the step size of the next time step, where, is the step size of the next time step, is the step size of the current time step, is the first control parameter, 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, using a second formula: , reset the step size of the current time step, where, is the reset time step size of the current time step, is the preset minimum time step, 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 when the norm satisfies at least one of the relative convergence reference and the absolute convergence reference, the solution result is determined to be a successful solution.

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

[0010] Optionally, a Newton-Krylov algorithm based on a field splitting preconditioner is used to solve the carbon storage mathematical model to obtain a 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 inexact Newton direction of the discrete mass conservation equation to obtain a search direction, and using a line search technique to determine the search step size of the discrete mass conservation equation to obtain an optimal step size; determining a 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: continuing 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.

[0012] According to another aspect of the present application, a simulation and solution device for carbon sequestration in deep saline water layers on land is provided, comprising: an acquisition unit for executing an acquisition step: acquiring carbon sequestration data of deep saline water layers on land, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is collected by applying corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation; a first determination unit for executing a first determination step: determining the mass fraction of components in each phase in the carbon storage mathematical model at a current time step based on the carbon sequestration data and a flash evaporation equation, and obtaining component mass fractions, 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 for executing a solution processing step: based on the component mass fractions, A Newton-Krylov algorithm based on a field splitting preconditioner is used to solve the carbon storage mathematical model to obtain a solution result, wherein the solution result includes the carbon storage pressure, carbon dioxide mole fraction and water mole fraction in the next time step, and the carbon storage pressure includes gas phase pressure and liquid phase pressure; a second determination unit is used to perform a second determination step: determining the step size of the next time step according to the solution result; an execution unit is used to perform a processing step: repeating the first determination step, the solution processing step and the second determination step in sequence at least once until the preset simulation time is reached to obtain a carbon sequestration simulation result; an evaluation unit is used to perform an evaluation step: applying the carbon sequestration simulation result to evaluate the carbon sequestration capacity of the deep onshore saline layer.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for determining carbon sequestration in deep onshore saline layers.

[0014] According to another aspect of the present 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, and the one or more programs include a method for executing any one of the determination methods for carbon sequestration in deep onshore saline aquifers.

[0015] Applying the technical solution of the present application, through the acquisition step: obtaining carbon sequestration data of deep saline water layers on land, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is collected by using corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation; the first determination step: determining the mass fraction of the components in each phase in the carbon storage mathematical model at the current time step based on the carbon sequestration data and the flash evaporation equation, and obtaining the component mass fraction, wherein the components include carbon dioxide, water and sodium chloride, and the phases include gas phase and liquid phase; the solution processing step: based on the component mass fraction, adopting a field splitting preconditioner The Newton-Krylov algorithm is used to solve the carbon storage mathematical model and obtain a solution result, wherein the solution result includes 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. The second determination step is to determine the step size of the next time step based on the solution result. The processing step is to repeat the first determination step, the solution processing step, and the second determination step at least once until the preset simulation time is reached to obtain the carbon storage simulation result. The evaluation step is to use the carbon storage simulation result to evaluate the carbon storage capacity of deep onshore saline aquifers. This solution, combined with the flash evaporation equation, can dynamically calculate the mass fractions of different components in different phases, reflecting the phase changes of components in actual geological environments. The use of 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 are prone to numerical oscillation when dealing with complex nonlinear systems of geological carbon sequestration, resulting in unreliable calculation results and inaccurate assessment of the carbon sequestration capacity of deep saline aquifers on land. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a determination method for carbon sequestration in deep saline aquifers on land provided in an embodiment of the present application is shown;

[0018] Figure 2 A flow chart of a method for determining carbon sequestration in deep saline water layers on land provided in an embodiment of the present application is shown;

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

[0020] Figure 4A schematic diagram of a process for fully implicitly solving the mass conservation equation provided in accordance with an embodiment of the present application is shown;

[0021] Figure 5 A structural block diagram of a determination device for carbon sequestration in deep saline layers on land provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, the existing technology is prone to numerical oscillations when dealing with complex nonlinear systems of geological carbon sequestration, resulting in unreliable calculation results, and thus inaccurate assessment of the carbon sequestration capacity of deep onshore saline aquifers. In order to solve the problem that the existing technology is prone to numerical oscillations when dealing with complex nonlinear systems of geological carbon sequestration, resulting in unreliable calculation results, and thus inaccurate assessment of the carbon sequestration capacity of deep onshore saline aquifers, the embodiments of the present application provide a determination method, device, computer-readable storage medium and electronic device for onshore deep onshore saline aquifer carbon sequestration.

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

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware block diagram of a mobile terminal for determining carbon sequestration in deep saline aquifers on land according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0028] Memory 104 can be used to store computer programs, such as application software programs and modules, such as the computer program corresponding to the method for determining carbon sequestration in deep saline aquifers on land in an embodiment of the present invention. Processor 102 executes the computer program stored in memory 104 to perform various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located from processor 102, which can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. Transmission device 106 is used to receive or transmit data via a network. Specific examples of such networks may include a wireless network provided by the mobile terminal's telecommunications provider. In one example, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication 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] In this embodiment, a method for determining carbon sequestration in deep onshore saline aquifers is provided, which runs on a mobile terminal, a computer terminal, or a similar computing device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 2 Flowchart of a method for determining carbon sequestration in deep saline aquifers on land according to an embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0031] Step S201, an acquisition step: obtaining carbon sequestration data of an onshore deep saline layer, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is collected using corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation;

[0032] The numerical simulation of carbon dioxide (CO2) storage in saline aquifers involves studying a multiphase, multicomponent system consisting of a rock matrix and pore fluids (CO2, brine, etc.). The mathematical model primarily includes basic governing equations and phase equilibrium calculation methods. These basic governing equations include mass conservation, energy conservation, stress equilibrium, and chemical reactions. Here, it is assumed that the governing equation is solely mass conservation, that the phases are gas and liquid, and that the components are carbon dioxide, water, and sodium chloride (NaCl). The mass conservation equation is a fundamental physical law that describes the invariance of the total amount of matter in the system, ensuring that the masses of carbon dioxide, water, and sodium chloride are conserved in both the gas and liquid phases during the simulation.

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

[0034] In step S202 above, combined with the flash evaporation equation, this solution can dynamically calculate the mass fractions of different components in different phases, reflecting the phase changes of components in actual geological environments.

[0035] Step S203, a solution processing step: based on the mass fractions of the components, a Newton-Krylov algorithm based on a field splitting preconditioner is used to solve the carbon storage mathematical model to obtain a solution result, wherein the solution result includes 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;

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

[0037] Step S204, second determination step: determining the step size of the next time step according to the above solution result;

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

[0039] Step S205, processing step: repeating the first determination step, the solution processing step, and the second determination step at least once in sequence until a preset simulation time is reached, thereby obtaining a carbon sequestration simulation result;

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

[0041] Step S206, evaluation step: using the above carbon sequestration simulation results to evaluate the carbon sequestration capacity of the above-mentioned deep saline water layer on land.

[0042] By employing a Newton-Krylov adaptive time algorithm based on a field splitting (FS) preconditioner, this method inherits the advantage of fully implicit solution methods in terms of unlimited time step size while improving algorithm stability. Compared to existing techniques, this method offers greater flexibility in time step selection. By combining the Newton-Rapson method, a standard solution strategy for reservoir simulation, with Krylov subspace techniques, this iterative method improves both robustness and efficiency.

[0043] Through this embodiment, applying the above-mentioned steps S201, S202, S203, S204, S205, and S206, the advantage of combining the FS preconditioner with the Newton-Krylov method is that it combines multiple solution methods, fully leveraging the advantages of each, and can efficiently solve complex nonlinear systems with good scalability and parallel computing capabilities. In combination with the flash evaporation equation, this solution can dynamically calculate the mass fractions of different components in different phases, reflecting the phase changes of components in actual geological environments. The use of 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 the existing technology is prone to numerical oscillation when dealing with complex nonlinear systems of geological carbon sequestration, resulting in unreliable calculation results and thus inaccurate assessment of the carbon sequestration capacity of deep saline aquifers on land.

[0044] In a specific implementation process, the second determination step: determining the step size of the next time step according to the above solution result, includes: determining whether the above solution result indicates that the solution is successful; if the above solution result indicates that the solution is successful, using the first formula: , determine the above step size of the next time step, where, is the above step size for the next time step, is the step size of the current time step mentioned above, is the first control parameter, The preset maximum time step.

[0045] In this technical solution, by checking the norm of the residual vector of the solution result, it is possible to determine whether the simulation process has converged, thereby determining whether the solution is successful. If the solution is successful, the step size of the next time step will be dynamically adjusted according to the step size of the current time step, the first control parameter, and the preset maximum time step to optimize the simulation efficiency. This method avoids numerical instability caused by excessively large step sizes or waste of computing resources caused by excessively small step sizes during the simulation process by intelligently adjusting the time step size, thereby achieving high efficiency and stability of 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 effect.

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

[0047] During the simulation process, if the solution indicates 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 the second formula to ensure stable simulation progress. The preset minimum time step size and the second control parameter provide a safety net for the simulation, preventing simulation failures caused by 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 sudden changes in geological structure or drastic changes in injection conditions, reducing the time step size can more precisely capture these changes, thereby increasing the credibility of the simulation results.

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

[0049] This technical solution determines whether the simulation has reached the convergence standard, that is, whether the solution is successful, by calculating the residual vector norm of the solution result and comparing it with the preset relative convergence reference and absolute convergence reference. The norm of the residual vector reflects the degree of deviation between the model solution result and the actual physical process and is a key indicator for evaluating simulation accuracy. The setting of relative convergence reference and absolute convergence reference provides a set of quantitative standards for simulation, ensuring the accuracy and reliability of the simulation results. This method solves the problem of unclear convergence judgment criteria in traditional simulations and improves the scientific nature and effectiveness of simulations.

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

[0051] This technical solution uses the fugacity coefficient method and the activity coefficient method, combined with the flash evaporation equation, to accurately calculate the mass fraction of water in the gas phase and carbon dioxide in the liquid phase. This is a key step in simulating the storage process of carbon dioxide 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 phase and liquid phase, 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 distribution of components between different phases and ensure the conservation of mass of components between the gas phase and liquid phase during the simulation process. This method solves the problem of inaccurate description of component phase changes in traditional simulations and improves the physical reliability of the simulation results. In addition, 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, geothermal resource utilization, etc. By accurately calculating the mass fractions 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.

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

[0053] This technical solution achieves the precise discretization and solution of the mass conservation equation in the carbon storage mathematical model through the efficient solution strategy of the Newton-Krylov algorithm. The Newton-Krylov algorithm combines the local convergence of the Newton method and the global search capability of the Krylov subspace method. By calculating the non-exact Newton direction and using line search technology to determine the optimal step size, it can quickly find a solution that meets the Newton 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, and provides an efficient and accurate calculation 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, groundwater resource management, etc. By optimizing the solution algorithm, the efficiency of the 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 significant advantages in dealing with complex geological problems.

[0054] More specifically, after resetting the step size of the current time step, the method further includes: continuing 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 mass fraction of the component.

[0055] This technical solution ensures simulation stability and accuracy by resetting the time step when a solution fails. After resetting the time step, the Newton-Krylov algorithm based on the field-splitting preconditioner is used to solve the problem, leveraging the algorithm's efficient solution capabilities and quickly restoring simulation convergence. This approach addresses the lack of effective strategies for dealing with numerical instabilities in traditional simulations, improving the robustness and practicality of the simulation.

[0056] In addition, this solution also incorporates the application of dynamic mesh adaptation technology, integrating it to enhance the model's simulation accuracy and computational efficiency for complex geological structures within saline aquifers. Dynamic mesh adaptation dynamically adjusts the mesh resolution based on changes in physical fields (such as pressure and saturation), automatically increasing mesh density in areas of high change rates and reducing it in areas of moderate change. This significantly reduces computational costs and resource consumption while maintaining model accuracy. Specific implementations are as follows:

[0057] In actual saline aquifer carbon storage simulations, an initial mesh is first established based on initial geological parameters (such as permeability and porosity). During each iterative solution step, dynamic mesh adaptation, based on the solution of the Newton-Krylov algorithm using the FS preconditioner, evaluates the rate of change of physical quantities within each grid cell. 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 a high-precision solution in highly dynamic regions of CO2 injection while maintaining computational efficiency in relatively stable regions.

[0058] This solution also includes a machine learning-based preprocessing method for predicting and optimizing the initial time step and preconditioner parameters, thereby accelerating the convergence of the Newton-Krylov algorithm and reducing the number of iterations. This method analyzes historical simulation data and trains a machine learning model to predict the most appropriate initial conditions and parameters to quickly reach a convergence state. Specific implementation examples are as follows:

[0059] During the development phase, a large amount of case data from saline aquifer carbon storage simulations was collected, including simulation results under different geological characteristics, injection conditions, and physical parameters. Based on this data, a regression model was trained. The model predicts an initial time step and optimal preconditioner parameter settings (such as α and β) for the Newton-Krylov algorithm of the FS preconditioner, based on the input geological and physical parameters. At the start of the simulation, the model automatically adjusts the time step and preconditioner parameters based on the current geological conditions, eliminating the traditional trial-and-error approach.

[0060] In simulation tests, using a machine learning-based preprocessing method reduced the number of Newton-Krylov algorithm iterations by an average of approximately 40% compared to traditional fixed parameter settings, significantly accelerating the solution. Furthermore, this method improved simulation stability and reduced non-convergence, particularly under the complex conditions of heterogeneous reservoirs.

[0061] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the determination method for carbon sequestration in deep onshore saline layers of the present application will be described in detail below with reference to specific embodiments.

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

[0063] The numerical simulation of CO2 storage in saline aquifers involves studying a multiphase, multicomponent system consisting of a rock solid skeleton and pore fluids (CO2, brine, etc.). The mathematical model primarily includes basic governing equations and phase equilibrium calculation methods. These basic governing equations include mass conservation equations, energy conservation equations, stress equilibrium equations, and chemical reactions. Here, it is assumed that the governing equations are solely mass conservation equations and energy conservation equations, that the phases are gas and liquid, and that the components are carbon dioxide, water, and NaCl.

[0064] The mass conservation equation is:

[0065] ;

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

[0067] is the flux of fluid component K:

[0068] ;

[0069] Where k is the absolute permeability, is the relative permeability of the α phase, generally between 0 and 1; is the viscosity of the α phase, which is a physical quantity that measures the viscosity of the fluid; is the pressure of the α phase, g is the acceleration due to gravity, is the diffusion and dispersion tensor, i.e. the diffusion of matter due to concentration gradient.

[0070] Gas phase pressure Liquid pressure The pressure difference is the capillary pressure ( Generally, it is experimental data, obtained by interpolation);

[0071] in = - , , is the mass fraction of NaCl, is the molecular mass of NaCl;

[0072] Water saturation , gas saturation The sum of is 1, for The mass fraction of the kth component in the phase, the sum of the mass fractions of each component is 1:

[0073] ;

[0074] ;

[0075] Where w represents the water 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 phase and the liquid phase, where the gas phase is calculated using the fugacity coefficient method, and the formula is as follows:

[0077] ;

[0078] in, is the fugacity coefficient of component m in the α phase, is the mole fraction of component l in the α phase, is the empirical factor of component l, subscript , subscript m is the component carbon dioxide or water, 、 is the empirical factor of the pure component m, 、 for The empirical factor of the phase, is the binary interaction parameter between components, Nc is the number of components, is the compression coefficient, and They represent the dimensionless amount of attraction and repulsion between molecules respectively.

[0079] The liquid phase is calculated using the activity coefficient, and the formula is as follows:

[0080] Activity coefficient ;

[0081] in is the molar concentration of NaCl, ;

[0082] coefficient ;

[0083] coefficient ;

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

[0085] The mole fractions of Co2 in the liquid phase and H20 in the gas phase can be calculated by the above-mentioned fugacity and activity methods, respectively. The mass fraction V of H20 in the gas can then be calculated by the flash evaporation equation. The equation is:

[0086] ;

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

[0088] This system of nonlinear coupled partial differential equations describes the multiphase, multicomponent problem of carbon sequestration. This system of equations encompasses the mass conservation and momentum equations for each component, as well as the phase equilibrium equations between the components. It describes the flow, diffusion, interaction, and transformation of the components in saline aquifers.

[0089] The adaptive time step is set as Figure 3 As shown, including the following:

[0090] Preset minimum time step t min With the maximum time step t max , and control parameters α, β. If the solution is successful at the current moment, the next time step is min(t max , α× the length of the previous time step); if the solution fails at the current moment, the time step is reset to max(t min , β×the length of 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 can automatically adjust the time step size, solving the problem of non-convergence due to too large a time step, and overcoming the load imbalance problem. Figure 4 As shown, specifically including the following contents:

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

[0093] According to the flash evaporation equation, calculate the mass fraction of the component in each phase;

[0094] Use the Newton-Krylov algorithm to solve the conservation equations: Use the Newton-Krylov algorithm based on the field splitting (FS) preconditioner to solve the conservation equations and update the solution vector for the next time step;

[0095] Update Time Step: Uses an adaptive time stepping method to set the time step size for each step until the final simulation time is reached.

[0096] Through the above content, this embodiment can effectively solve the component equation, give full play to the advantages of each, and obtain stable and accurate results.

[0097] The Newton-Krylov algorithm, a commonly used parallel solution method for solving large-scale nonlinear equations, is also highly favored. This algorithm combines the Newton method and the Krylov subspace method. The detailed steps of the algorithm are described below:

[0098] First, the Newton-Krylov method is used to discretize the nonlinear partial differential equation. Represents the initial solution vector, then the next approximate solution of the Newton-Krylov algorithm is The update is as follows, the specific steps are:

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

[0100] Step S2: (Determine the search direction) Calculate the inexact Newton direction , such that:

[0101] ,in is the Jacobian matrix, is the preconditioner, is the inverse of the preconditioner, 、 are relative error and absolute error respectively, is the nonlinear equation system of the entire model.

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

[0103] Step S4: (Stop condition) Update the approximate solution , determine the Newton iteration stopping index: , otherwise, set k=k+1 and execute step S2; ε r and ε a They represent the relative error and absolute error of Newton iteration respectively.

[0104] Among them, in the Newton-Krylov method, one of the most important components of the solver is to choose a suitable preconditioner, because the linearized system is usually difficult to solve. Therefore, in step S2 It is a FS preconditioner that can better solve linear equations.

[0105] FS preconditioner uses relaxation or factorization to solve Linear block system, for simplicity, restrict the matrix to The Jacobi matrix is represented as follows:

[0106]

[0107] The following introduces a special FS preconditioner, also called 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. and The approximate inverse action of the system is and Matrix of , first stage preconditioner for:

[0108]

[0109] The second stage preconditioner approximates the original complete system, so it is approximately the inverse of the Jacob matrix, that is, ;

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

[0111] use Preconditioner: ;

[0112] Compute the new residuals: ;

[0113] use Preconditioner and fix: ;

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

[0115]

[0116] in, and The Schwarz preconditioner is a technique based on domain decomposition that divides the solution domain into multiple subdomains and constructs an independent preconditioner on each subdomain. By fully leveraging the structural characteristics and local information of the linear 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] , is the number of processors, T is the transpose symbol, is the subdomain Jacobian matrix The approximate inverse of They are the restriction operator and the interpolation operator respectively;

[0118] When solving linear systems, the GMRES method constructs a Krylov subspace at each iteration and gradually approximates the exact solution to find the linear combination of the minimum residual vector. Given its wide applicability, good iterative convergence, and efficient memory utilization, we chose the GMRES method for solving linear systems.

[0119] In summary, the advantage of the combination of the FS preconditioner and the Newton-Krylov method is that it combines multiple solution methods, giving full play to the advantages of each, can efficiently solve complex nonlinear systems, and has good scalability and parallel computing capabilities.

[0120] This embodiment uses a Newton-Krylov adaptive time algorithm based on a field splitting (FS) preconditioner. This solution inherits the advantage of the fully implicit solution method in that the time step is not restricted, and improves the stability of the algorithm. Compared with existing technologies, this solution has greater flexibility in the selection of time steps. The Newton-Rapson method and Krylov subspace technology, which are standard solution strategies in reservoir simulation, are combined to improve the robustness and efficiency of the iterative method. In addition, the Schwarz preprocessing technology is applied to the preprocessing stage of the linear iteration to accelerate the convergence of the algorithm. In summary, this embodiment can be effectively applied to simulation methods for large-scale parallel solutions to multi-phase and multi-component seepage problems.

[0121] In a practical application scenario, the proposed solution was used to perform reservoir numerical simulation. The fully implicit solution method proposed in this embodiment was implemented on a computer using the PETSc toolkit. Experimental validation of the proposed method in saline aquifer storage tests demonstrated that the mass fraction of carbon dioxide in saline water fully matched the reference solution calculated in MOOSE, and the numerical results were stable. This method supports multi-core computing and is suitable for large-scale parallel solutions to multiphase and multicomponent problems in reservoir simulation.

[0122] The embodiment of the present application also provides a determination device for carbon sequestration in deep onshore saline aquifers. It should be noted that the determination device for carbon sequestration in deep onshore saline aquifers in the embodiment of the present application can be used to execute the determination method for carbon sequestration in deep onshore saline aquifers provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementations, and those that have been explained will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0123] The following introduces the determination device for carbon sequestration in deep saline water layers on land provided in the embodiments of the present application.

[0124] Figure 5 Schematic diagram of a device for determining carbon sequestration in deep saline water layers on land according to an embodiment of the present application. Figure 5 As shown, the device includes:

[0125] an acquisition unit 51 configured to perform an acquisition step of acquiring carbon sequestration data of an onshore deep saline layer, 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;

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

[0127] a solution processing unit 53 configured to perform a solution processing step: based on the mass fractions of the components, using a Newton-Krylov algorithm based on a field splitting preconditioner, to solve the carbon storage mathematical model to obtain a solution result, wherein the solution result includes the carbon storage pressure, the carbon dioxide mole fraction, and the water mole fraction at the next time step, and the carbon storage pressure includes the gas phase pressure and the liquid phase pressure;

[0128] The second determining unit 54 is configured to perform a second determining step: determining the step size of the next time step according to the solution result;

[0129] An execution unit 55 is configured to execute a processing step: sequentially and repeatedly executing the first determining step, the solution processing step, and the second determining step at least once until a preset simulation time is reached, thereby obtaining a carbon sequestration simulation result;

[0130] The evaluation unit 56 is configured to perform an evaluation step: using the carbon sequestration simulation results to evaluate the carbon sequestration capacity of the above-mentioned deep saline layer on land.

[0131] In this embodiment, an acquisition unit is applied to perform an acquisition step: obtaining carbon sequestration data of an onshore deep saline layer, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is collected by applying a corresponding sensor, and the carbon storage mathematical model includes a mass conservation equation; a first determination unit is applied to perform a first determination step: determining the mass fraction of a component in each phase in the carbon storage mathematical model at a current time step based on the carbon sequestration data and the flash evaporation equation, and obtaining a 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 applied to perform a solution processing step: based on the component mass fraction, using Ne based on a field splitting preconditioner The Wton-Krylov algorithm is used to solve the carbon storage mathematical model to obtain a solution result, wherein the solution result includes the carbon storage pressure, carbon dioxide mole fraction and water mole fraction in the next time step, and the carbon storage pressure includes gas phase pressure and liquid phase pressure; a second determination unit is applied to perform the second determination step: determining the step size of the next time step according to the solution result; an execution unit is applied to perform the processing step: repeating the first determination step, the solution processing step and the second determination step in sequence at least once until the preset simulation time is reached to obtain a carbon sequestration simulation result; an evaluation unit is applied to perform the evaluation step: using the carbon sequestration simulation result to evaluate the carbon sequestration capacity of the deep saline layer on land.

[0132] As an optional solution, the second determination unit includes a first determination module and a second determination module; the first determination module is used to determine whether the above solution result indicates that the solution is successful; the second determination module is used to use the first formula when the above solution result indicates that the solution is successful: , determine the above step size of the next time step, where, is the above step size for the next time step, is the step size of the current time step mentioned above, is the first control parameter, The preset maximum time step.

[0133] In an optional solution, the second determining unit further includes a reset module, which is configured to use a second formula when the above solution result indicates that the solution has failed: , reset the step size of the current time step above, where, is the reset time step size of the current time step, is the preset minimum time step, is the second control parameter.

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

[0135] In an optional solution, the first determination unit includes a third determination module and a fourth determination module; the third determination module is used to determine the first mole fraction of the water in the gas phase using a fugacity coefficient method, and to determine the second mole fraction of the carbon dioxide in the liquid phase using an activity coefficient method; the fourth determination module is used to determine the mass fraction of the component 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 a flash evaporation equation.

[0136] An optional solution, 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 above-mentioned mass conservation equation in the above-mentioned carbon storage mathematical model using the Newton-Krylov algorithm to obtain the discrete mass conservation equation; the first calculation module is used to calculate the non-exact Newton direction of the above-mentioned discrete mass conservation equation to obtain a search direction, and use a line search technology to determine the search step size of the above-mentioned discrete mass conservation equation to obtain an 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] An optional solution is that the second determination unit further includes a solution processing module for, after resetting the step size of the current time step, continuing to use the Newton-Krylov algorithm based on the field splitting preconditioner to solve the above-mentioned carbon storage mathematical model based on the reset step size of the current time step and the above-mentioned component mass fraction.

[0138] The above-mentioned determination device for carbon sequestration in deep 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 implement the corresponding functions. The above-mentioned modules are all located in the same processor; alternatively, the above-mentioned modules can be located in different processors in any combination.

[0139] The processor includes a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured. By adjusting kernel parameters, this solves the problem of numerical oscillations that occur when processing complex nonlinear systems for geological carbon storage in existing technologies. This leads to unreliable calculation results and inaccurate assessments of the carbon storage capacity of deep saline aquifers on land.

[0140] The memory may include non-permanent memory in a computer-readable medium, 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] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is run, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned determination method for carbon sequestration in deep onshore saline water layers.

[0142] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program, when running, executes the above-mentioned determination method for carbon sequestration in deep onshore saline aquifers.

[0143] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least the steps of the above-mentioned method for determining carbon sequestration in deep saline water layers on land are implemented.

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

[0145] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes at least the steps of the above-mentioned determination method for carbon sequestration in deep onshore saline aquifers.

[0146] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. They can be implemented using program code executable by the computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or 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 appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

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

[0152] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0153] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0154] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0155] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for determining carbon sequestration in deep saline aquifers on land, characterized in that: include: Acquisition step: acquiring carbon sequestration data of an onshore deep saline layer, and constructing a carbon storage mathematical model based on the carbon sequestration data, wherein the carbon sequestration data is collected using corresponding sensors, and the carbon storage mathematical model includes a mass conservation equation; A first determining step: determining, based on the carbon sequestration data and the flash evaporation equation, the mass fraction of a component in each phase in the carbon storage mathematical model at a current time step to obtain 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; Solving step: solving the carbon storage mathematical model based on the mass fractions of the components using a Newton-Krylov algorithm based on a field splitting preconditioner to obtain a solution result, wherein the solution result includes the carbon storage pressure, the carbon dioxide mole fraction, and the water mole fraction at the next time step, and the carbon storage pressure includes the gas phase pressure and the liquid phase pressure; Second determination step: determining the step size of the next time step according to the solution result; Processing step: repeating the first determining step, the solution processing step, and the second determining step at least once in sequence until a preset simulation time is reached to obtain a carbon sequestration simulation result; Evaluation step: using the carbon sequestration simulation results to evaluate the carbon sequestration capacity of the onshore deep saline layer.

2. The method according to claim 1, characterized in that The second determination step: determining the step size of the next time step according to the solution result, including: Determining whether the solution result indicates a successful solution; When the solution result indicates that the solution is successful, the first formula is used: , determine the step size of the next time step, where, is the step size of the next time step, is the step size of the current time step, is the first control parameter, 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: When the solution result indicates that the solution has failed, the second formula is used: , reset the step size of the current time step, where, is the reset time step size of the current time step, is the preset minimum time step, 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: Determining a residual vector of the solution result and determining a 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, wherein when the norm satisfies at least one of the relative convergence reference and the absolute convergence reference, determine that the solution result is a successful solution.

5. The method according to claim 1, wherein According to the flash evaporation equation, determine the mass fraction of the components in each phase in the carbon storage mathematical model at the current time step, including: determining a first mole fraction of the water in the gas phase using a fugacity coefficient method, and determining a second mole fraction of the carbon dioxide in the liquid phase using an activity coefficient method; Based on the first mole fraction and the second mole fraction and according to a 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 carbon storage mathematical model is solved using a Newton-Krylov algorithm based on a field splitting preconditioner to obtain the following solution: discretizing the mass conservation equation in the carbon storage mathematical model using a Newton-Krylov algorithm to obtain a discrete mass conservation equation; Calculating the inexact Newton direction of the discrete mass conservation equation to obtain a search direction, and using a line search technique to determine the search step size of the discrete mass conservation equation to obtain an optimal step size; A Newton iteration stopping criterion is determined based on the search direction and the optimal step size, 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 mass fraction of the component, the Newton-Krylov algorithm based on the field splitting preconditioner is continued to be used to solve the carbon storage mathematical model.

8. A simulation solution device for carbon sequestration in deep saline aquifers on land, characterized by: include: an acquisition unit, configured to perform an acquisition step of acquiring carbon sequestration data of an onshore deep saline layer, 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 determining unit configured to perform a first determining step of determining, based on the carbon sequestration data and a flash evaporation equation, a mass fraction of a component in each phase in the carbon storage mathematical model at a current time step to obtain a 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, configured to perform a solution processing step: based on the mass fractions of the components, using a Newton-Krylov algorithm based on a field splitting preconditioner to solve the carbon storage mathematical model to obtain a solution result, wherein the solution result includes the carbon storage pressure, the carbon dioxide mole fraction, and the water mole fraction at the next time step, and the carbon storage pressure includes the gas phase pressure and the liquid phase pressure; A second determining unit is configured to perform a second determining step: determining the step size of the next time step according to the solution result; an execution unit, configured to execute a processing step: sequentially and repeatedly executing the first determining step, the solution processing step, and the second determining step at least once until a preset simulation time is reached, thereby obtaining a carbon sequestration simulation result; An evaluation unit is used to perform an evaluation step: using the carbon sequestration simulation result to evaluate the carbon sequestration capacity of the onshore deep saline layer.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the determination method for carbon sequestration in deep onshore saline aquifers according to 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, and the one or more programs include 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.

Images