Modeling method for low-permeability complex porous medium molecules
By performing three-dimensional expansion and simulated annealing on silica crystals, a porous media model with tunable hydrophilicity and hydrophobicity was constructed, solving the problem that existing technologies cannot effectively characterize the pore network of low-permeability reservoirs. This enabled high-fidelity reconstruction of natural reservoirs and accurate simulation of phase change flow coupling processes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-03-24
AI Technical Summary
Existing molecular simulation models for porous media cannot effectively characterize the complex pore network structure in low-permeability reservoirs, resulting in discrepancies between molecular simulation results and actual reservoir processes, which limits their application depth in unconventional energy development.
The CLAYFF force field is used to extend the silica crystal in three dimensions. Amorphous silica is generated by simulated annealing. Then, three-dimensional stretching and surface end-capping are performed to construct a porous medium model with tunable hydrophilicity and hydrophobicity. Water molecules or gas molecules are input to obtain the target porous medium molecular model.
The constructed porous medium molecular model can realistically reflect the complexity of the pore structure of the natural reservoir framework, improve the application depth of molecular simulation in hydrates and other unconventional energy systems, and realize the accurate simulation of phase change and seepage processes.
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Figure CN121725898A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of molecular modeling of porous media, and specifically to a method for molecular modeling of low-permeability complex porous media. Background Technology
[0002] The efficient development of unconventional energy sources still faces numerous severe scientific and engineering challenges in the current energy field, with the problems posed by unconventional gas energy, represented by natural gas hydrates, being particularly prominent and intractable. One major difficulty lies in the fact that the phase transition processes and fluid-solid production mechanisms within the reservoirs are not yet fully understood. During the extraction of natural gas hydrates, a series of extremely complex dynamic processes, such as decomposition, migration, and regeneration, are involved. These processes are constrained and influenced by multiple factors, including pore connectivity, wettability, capillary pressure, and thermo-mass coupling. This complex multi-factor interaction leads to significant deviations in the prediction of gas production efficiency and introduces considerable uncertainty into the optimization of extraction schemes, posing significant difficulties to actual extraction operations. Therefore, revealing the coupling mechanism between phase transition and seepage in hydrate reservoirs at the microscopic scale has become a crucial scientific issue for deeply understanding the hydrate extraction process and improving its development efficiency.
[0003] Molecular simulation, as a crucial tool for studying the microscopic behavior of multiphase systems, plays an irreplaceable role in scientific research. It can precisely track the evolution of gas, water, and hydrate phase interfaces at the atomic scale, revealing the influence of factors such as pore structure, surface wettability, and capillary forces on fluid phase transitions and transport processes. Compared to macroscopic experiments and continuum models, molecular simulation possesses unique advantages, directly characterizing detailed information such as microscopic interactions, interfacial energies, and kinetic pathways. This provides high-resolution theoretical support for in-depth exploration of thermodynamic and kinetic processes under complex reservoir conditions, greatly advancing research progress in related fields. Therefore, molecular simulation has gradually developed into an indispensable tool for studying the behavior of porous fluids in unconventional energy systems, playing an increasingly important role in exploring the mysteries of unconventional energy development.
[0004] However, current molecular simulation models for porous media still have significant limitations. Existing methods mostly employ crystalline silicon frameworks, creating pore spaces by drilling holes or using two pieces of crystalline silicon to form pore walls. These models are overly idealized and simplistic, failing to reflect the amorphous framework characteristics, pore heterogeneity, and anisotropy prevalent in natural sedimentary reservoirs. Particularly in low-permeability reservoirs such as siltstone and mudstone, the pore structure is complex, connectivity is poor, and surface chemical composition is diverse. Traditional models cannot effectively characterize the true features of their microporous networks, leading to discrepancies between molecular simulation results and actual reservoir processes. This limits the application depth of molecular simulations in hydrates and other unconventional energy systems. Summary of the Invention
[0005] The purpose of this application is to overcome the shortcomings and deficiencies of the prior art and provide a molecular modeling method for low-permeability complex porous media.
[0006] The first aspect of this application provides a method for molecular modeling of low-permeability complex porous media, including:
[0007] Based on the selected CLAYFF force field, the silicon dioxide crystal is expanded in three dimensions to obtain a supercell of a preset size.
[0008] The supercell is subjected to simulated annealing according to preset heating and cooling parameters to obtain amorphous silicon dioxide;
[0009] The amorphous silica system was stretched and expanded in three dimensions according to its overall porosity to obtain a porous medium model;
[0010] The required number of hydroxyl groups to be added is determined based on the pore surface area of the porous medium model, and hydroxyl groups are added to the silicon atoms of the porous medium model for surface sealing to obtain the first porous medium model.
[0011] The number of hydrophilic hydroxyl groups / hydrophobic methyl groups in the first porous medium model is adjusted and / or replaced to obtain a second porous medium model with adjustable hydrophilicity and hydrophobicity.
[0012] Water molecules are input or gas molecules are injected into the second porous medium model to obtain the target porous medium molecular model.
[0013] As one implementation method, the step of simulating annealing the supercell according to preset heating and cooling parameters to obtain amorphous silicon dioxide includes:
[0014] Within a preset time period, the supercell is heated to 7000K and then cooled to room temperature to obtain the amorphous silicon dioxide.
[0015] As one implementation method, after the step of simulating annealing the supercell according to preset heating and cooling parameters to obtain amorphous silicon dioxide, the method includes:
[0016] The radial distribution functions of silicon-oxygen bonds, silicon-silicon bonds, and oxygen-oxygen bonds in the amorphous silicon dioxide were calculated to obtain the corresponding radial distribution simulation parameters.
[0017] If the difference between the simulated radial distribution parameter and the corresponding experimental radial distribution value is within a preset deviation range, the step of performing three-dimensional spatial stretching and expansion on the amorphous silica system according to the overall porosity is executed.
[0018] As one implementation, after calculating the radial distribution functions of silicon-oxygen bonds, silicon-silicon bonds, and oxygen-oxygen bonds in the amorphous silicon dioxide to obtain the corresponding radial distribution simulation parameters, the step includes:
[0019] If the difference between the simulated radial distribution parameter and the corresponding experimental radial distribution value exceeds the deviation range, amorphous silicon dioxide is obtained again.
[0020] As one implementation method, the step of performing three-dimensional spatial stretching and expansion on the amorphous silica system according to its overall porosity to obtain a porous medium model includes:
[0021] The size of the amorphous silica system box is dynamically adjusted so that the overall porosity of the system reaches the preset target value;
[0022] Annealing simulation was performed on the amorphous silica whose overall porosity reached the preset target value, which caused the silicon-oxygen framework to rearrange, resulting in a porous medium model whose overall porosity reached the preset target value and whose framework was stable.
[0023] As one implementation method, after performing three-dimensional spatial stretching and expansion on the amorphous silica system according to the overall porosity to obtain a porous medium model, the method includes:
[0024] The porosity, pore surface area, average pore diameter, and connectivity parameters of the porous medium model are used to perform model standardization testing. If the test is passed, the number of hydroxyl groups to be added is obtained based on the pore surface area of the porous medium model. Hydroxyl groups are then added to the silicon atoms of the porous medium model to perform surface end capping, thereby obtaining the first porous medium model.
[0025] As one implementation method, after performing model standardization testing based on the porosity, pore surface area, average pore diameter, and connectivity parameters of the porous media model, the method includes:
[0026] If it fails, obtain a new porous media model.
[0027] As one implementation method, the preset deviation range is ±5%.
[0028] In one embodiment, the gas molecules include CH4 and CO2.
[0029] Compared to related technologies, the molecular modeling method for low-permeability complex porous media in this application, based on the selected CLAYFF force field, expands silica crystals in three-dimensional space to obtain supercells of a preset size. Then, the supercells are subjected to simulated annealing according to preset heating and cooling parameters to obtain amorphous silica. The amorphous silica system is then stretched and expanded in three-dimensional space according to the overall porosity to obtain a porous media model. The number of hydroxyl groups to be added is obtained based on the pore surface area of the porous media model. Hydroxyl groups are added to the silicon atoms of the porous media model to perform surface end capping, resulting in a first porous media model. Adjusting and / or replacing the number of hydroxyl groups in the first porous media model, as well as introducing water molecules or injecting gas molecules, can obtain a target porous media molecular model that truly reflects the complexity of the pore structure of the natural reservoir framework, thus improving the depth of application of molecular simulation in hydrates and other unconventional energy systems.
[0030] To provide a clearer understanding of this application, the specific embodiments of this application will be described below in conjunction with the accompanying drawings. Attached Figure Description
[0031] Figure 1 This is a flowchart illustrating a molecular modeling method for low-permeability complex porous media according to an embodiment of this application.
[0032] Figure 2 This is a schematic diagram illustrating the steps of a molecular modeling method for low-permeability complex porous media according to an embodiment of this application.
[0033] Figure 3 This is a schematic diagram of amorphous silica, representing a molecular modeling method for low-permeability complex porous media according to an embodiment of this application.
[0034] Figure 4 This is a schematic diagram of pore size data for a molecular modeling method for low-permeability complex porous media according to an embodiment of this application. Detailed Implementation
[0035] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0036] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.
[0037] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."
[0038] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0039] Please see Figure 1 and Figure 2 , Figure 1 This is a schematic flowchart of the molecular modeling method for low-permeability complex porous media according to the first embodiment of this application. Figure 2 This is a schematic diagram of the steps of the molecular modeling method for low-permeability complex porous media according to the first embodiment of this application. The method includes:
[0040] S1: Based on the selected CLAYFF force field, the silicon dioxide crystal is expanded in three dimensions to obtain a supercell of a preset size;
[0041] The CLAYFF force field can accurately characterize the structure and interaction features of silicon-oxygen systems. Both types of non-bonded interaction parameters of silicon and oxygen atoms (Lennard-Jones potential parameters and charge parameters) can be directly obtained from the force field parameter file. The potential function form of the CLAYFF force field can be expressed as:
[0042]
[0043] in, Represents the Lennard-Jones potential function; For Coulomb interaction; The potential well depth is expressed in kcal / mol. This represents the distance at which particle interactions are zero, measured in Å. This represents the distance between different particles, in Å. This represents the distance between particle i and particle j; Represents the vacuum permittivity; , These represent the charge of particles i and j, respectively, in units of carbon (C).
[0044] The structural files of silicon dioxide crystals can be downloaded from crystallography data centers and then modeled using the molecular simulation software LAMMPS. The original unit cell is expanded in the x, y, and z directions using the replicate command to generate a supercell of the desired size.
[0045] S2: The supercell is subjected to simulated annealing according to preset heating and cooling parameters to obtain amorphous silicon dioxide.
[0046] Step S2 can utilize the `fix nvt` command in the molecular simulation software LAMMPS to rapidly heat and cool the system, specifically by quickly heating it to approximately 7000 K and then rapidly cooling it to room temperature, thereby achieving the amorphization of silicon dioxide and obtaining amorphous silicon dioxide. Please refer to [link to relevant documentation]. Figure 3 Taking β-cristobalite unit cells as an example, the obtained amorphous silicon dioxide is as follows: Figure 3 As shown
[0047] S4: The amorphous silica system is stretched and expanded in three dimensions according to the overall porosity to obtain a porous medium model;
[0048] The system's volume expansion rate is calculated based on a preset target porosity to adjust the overall porosity. For example, with a target porosity of 50%, the framework and pores each occupy 50% of the volume, and the simulated system should expand to approximately 1.26 times its original length in all directions. An adiabatic ensemble (NVE) is used, and the system is three-dimensionally stretched using the fix deform command. Subsequently, the system dimensions are fixed under a canonical ensemble (NVT) and annealed to room temperature to allow atomic structure recombination and relaxation stabilization. After energy minimization and isolated atom removal, a preliminary porous medium model (i.e., ...) is obtained. Figure 1 (Porous media framework in the middle).
[0049] S6: Obtain the required number of hydroxyl groups to be added based on the pore surface area of the porous medium model, and add hydroxyl groups to the silicon atoms of the porous medium model to perform surface sealing, thereby obtaining the first porous medium model.
[0050] Please see Figure 4In step S6, the Poreblazer software developed by the University of Manchester can be used to analyze the pore structure parameters of the porous media model, calculating indicators such as pore surface area, porosity, average pore diameter, median pore diameter, and mainstream pore diameter distribution, providing a basis for evaluating the rationality of the model structure. Based on the calculated pore surface area and the average proportion of hydroxyl groups on the surface of natural silica (approximately 50–70%), hydroxyl groups (–OH) are randomly added to the silicon atoms of the porous media model for surface sealing, reducing surface dangling bonds and improving the chemical stability of the model. Natural silica surfaces are mostly hydrophilic and predominantly hydroxyl-terminated. The hydroxyl group density on the surface of natural silica is typically... The average value can be used here. The required number of hydroxyl groups to be added is calculated based on the pore surface area obtained from the Poreblazer software, and then randomly added to the Si atoms on the framework surface. After end-capping, the first porous medium model is obtained.
[0051] S7: Adjust and / or replace the number of hydrophilic hydroxyl groups / hydrophobic methyl groups in the first porous media model to obtain a second porous media model with adjustable hydrophilicity and hydrophobicity.
[0052] In step S7, the hydrophilicity of the framework surface can be quantitatively controlled by adjusting the number of hydroxyl groups. Simultaneously, the degree of hydrophobicity of the surface can be altered by replacing some hydroxyl groups with methyl groups (-CH3) and adjusting the methyl coverage, thereby achieving controllable adjustment of the wettability of the model surface.
[0053] Specifically, hydrophilicity can be adjusted by modifying the number of -OH hydroxyl groups on the silica framework surface. A higher hydroxyl group density results in stronger hydrophilicity, and vice versa. Hydrophobicity can be adjusted by replacing the -OH hydroxyl groups with methyl groups (-CH3). Similarly, modifying the methyl group density alters the surface hydrophobicity; a higher density results in stronger hydrophobicity.
[0054] S8: Water molecules are input or gas molecules are injected into the second porous medium model to obtain the target porous medium molecular model.
[0055] In step S8, a certain number of water molecules or gas molecules (such as CH4, CO2, etc.) are added using the create_atoms command of LAMMPS to construct a water-containing or gas-containing system. Subsequently, the adsorption, migration and phase change behavior of fluids in the pores can be studied through molecular dynamics simulation. This is to obtain a target porous medium molecular model that can be used for multiphase fluid migration and phase change simulation, and can be used for molecular simulation studies of hydrate formation, decomposition and phase change flow.
[0056] Compared to related technologies, the molecular modeling method for low-permeability complex porous media in this application, based on the selected CLAYFF force field, expands silica crystals in three-dimensional space to obtain supercells of a preset size. Then, the supercells are subjected to simulated annealing according to preset heating and cooling parameters to obtain amorphous silica. The amorphous silica system is then stretched and expanded in three-dimensional space according to the overall porosity to obtain a porous media model. The number of hydroxyl groups to be added is obtained based on the pore surface area of the porous media model. Hydroxyl groups are added to the silicon atoms of the porous media model to perform surface end capping, resulting in a first porous media model. Adjusting and / or replacing the number of hydroxyl groups in the first porous media model, as well as introducing water molecules or injecting gas molecules, can obtain a target porous media molecular model that truly reflects the complexity of the pore structure of the natural reservoir framework, thus improving the depth of application of molecular simulation in hydrates and other unconventional energy systems.
[0057] In a feasible embodiment, S2: After the step of simulating annealing the supercell according to preset heating and cooling parameters to obtain amorphous silicon dioxide, the process includes:
[0058] S31: Calculate the radial distribution functions of silicon-oxygen bonds, silicon-silicon bonds, and oxygen-oxygen bonds in the amorphous silicon dioxide to obtain the corresponding radial distribution simulation parameters;
[0059] The built-in calculation program in LAMMPS can be used to calculate the radial distribution function (RDF) of silicon-oxygen bonds, silicon-silicon bonds, and oxygen-oxygen bonds.
[0060] S32: If the difference between the radial distribution simulation parameter and the corresponding experimental radial distribution value is within the preset deviation range, perform the step of three-dimensional spatial stretching and expansion of the amorphous silica system according to the overall porosity.
[0061] S33: If the difference between the radial distribution simulation parameter and the corresponding experimental radial distribution value exceeds the deviation range, amorphous silicon dioxide is obtained again.
[0062] The simulated radial distribution parameters are compared with the corresponding experimental radial distribution values. If there is a significant deviation, it indicates an error in the simulation process. The annealing simulation and verification should be repeated until the obtained structure matches the experimental radial distribution value. For example, the preset deviation range is ±5%. If the deviation between the radial distribution function and the experimental radial distribution value is within ±5%, the structure of the amorphous silicon dioxide is deemed reasonable, and step S4 can be performed on the amorphous silicon dioxide. If the deviation between the radial distribution function and the experimental radial distribution value exceeds ±5%, amorphous silicon dioxide is obtained again until the deviation between the radial distribution function and the experimental radial distribution value is within ±5%.
[0063] In a feasible embodiment, S4: the step of performing three-dimensional spatial stretching and expansion on the amorphous silica system according to the overall porosity to obtain a porous medium model includes:
[0064] S41: Dynamically adjust the size of the amorphous silicon dioxide system box to make the overall porosity of the system reach the preset target value;
[0065] S42: Annealing simulation is performed on the amorphous silicon dioxide whose overall porosity reaches the preset target value, causing the silicon-oxygen framework to rearrange, and obtaining the porous medium model whose overall porosity reaches the preset target value and whose framework is stable.
[0066] For example, the system box size can be dynamically adjusted using the `fix nve` command in the molecular simulation software LAMMPS to achieve a predetermined target value for the overall porosity. Subsequently, the `fix nvt` command is used to perform another annealing simulation. Through periodic heating and cooling processes, the silicon-oxygen framework is rearranged and its structure relaxed, resulting in a porous medium model with a reasonable pore distribution and a stable framework. The overall porosity of the system is adjustable within the range of 20%–80%.
[0067] In a feasible embodiment, S4: After the step of performing three-dimensional spatial stretching and expansion on the amorphous silica system according to the overall porosity to obtain a porous medium model, the following steps are included:
[0068] S51: Perform model standardization test based on the porosity, pore surface area, average pore diameter and connectivity parameters of the porous medium model. If the test is passed, obtain the number of hydroxyl groups to be added based on the pore surface area of the porous medium model, and add hydroxyl groups to the silicon atoms of the porous medium model to perform surface end capping, thereby obtaining the first porous medium model.
[0069] S52: If it fails, re-acquire the porous medium model.
[0070] The standardization test can be performed by comparing the porosity, pore surface area, average pore diameter, and connectivity parameters of the porous medium model with corresponding standard values. If they are the same, the test is passed; otherwise, the test is failed. Alternatively, the test can be performed using the following standards:
[0071] (1) In order to make the hydrate sediment system more reasonable, the pore size distribution should satisfy the Boltzmann distribution function to a certain extent;
[0072] (2) Taking into account the phase transition space of hydrates and computational capabilities, the median pore size should be controlled within a certain range (5 nm < r < 10 nm).
[0073] (3) Large-diameter pores in sediments are usually interconnected, i.e., they need to be connected by pore throats;
[0074] (4) Due to limitations in computing power, the sediment configuration (nanoscale) constructed in molecular simulation cannot reach the actual sediment particle size (micrometer scale) in terms of volume. However, the constructed molecular configuration should be consistent with the experimental test results as much as possible in terms of particle size distribution trend, pore size distribution state, pore throat connectivity and sediment stacking mode.
[0075] If all the criteria in (1)-(4) are not met, it means that the test is passed; otherwise, it means that the test is not passed and the porous medium model needs to be reconstructed.
[0076] In summary, the molecular modeling method for low-permeability complex porous media proposed in this application has the following advantages:
[0077] High-fidelity reservoir reconstruction capability: The porous medium molecular model constructed in this invention can realistically reflect the complexity of the pore structure of the natural reservoir framework, breaking through the limitations of single pore morphology in traditional modeling methods. This method achieves the greatest possible replication of the microscopic heterogeneous characteristics of the low-permeability silty mudstone reservoir in the South China Sea, laying a structural foundation for the accurate simulation of phase change-seepage coupling processes in the reservoir.
[0078] Dual optimization mechanism of structure and physical properties: By combining molecular dynamics annealing simulation technology with stretching structure reconstruction strategy, the synergistic regulation of pore morphology and surface chemical properties of the model is realized. Pore connectivity and surface wettability can be flexibly adjusted according to research needs, thereby improving the applicability and representativeness of the model.
[0079] A model rationality evaluation system was established: a set of systematic model evaluation standards were formulated to quantitatively characterize the model from two aspects: pore structure parameters (such as porosity, median pore size, and connectivity) and surface property parameters (such as wettability), thereby realizing a scientific evaluation of the rationality and effectiveness of the constructed porous medium molecular model.
[0080] The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.
[0081] 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.
[0082] 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 The 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 selected in one or more boxes.
[0083] 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 selected in one or more boxes.
[0084] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0085] 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.
[0086] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data, 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.
[0087] 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.
[0088] The above are merely embodiments of this application and are not intended to limit the scope of 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 scope of the claims of this application.
Claims
1. A molecular modeling method for low-permeability complex porous media, characterized in that, include: Based on the selected CLAYFF force field, the silicon dioxide crystal is expanded in three dimensions to obtain a supercell of a preset size. The supercell is subjected to simulated annealing according to preset heating and cooling parameters to obtain amorphous silicon dioxide; The amorphous silica system was stretched and expanded in three dimensions according to its overall porosity to obtain a porous medium model; The required number of hydroxyl groups to be added is determined based on the pore surface area of the porous medium model, and hydroxyl groups are added to the silicon atoms of the porous medium model for surface sealing to obtain the first porous medium model. The number of hydrophilic hydroxyl groups / hydrophobic methyl groups in the first porous medium model is adjusted and / or replaced to obtain a second porous medium model with adjustable hydrophilicity and hydrophobicity. Water molecules are input or gas molecules are injected into the second porous medium model to obtain the target porous medium molecular model.
2. The molecular modeling method for low-permeability complex porous media according to claim 1, characterized in that, The step of simulating annealing the supercell according to preset heating and cooling parameters to obtain amorphous silicon dioxide includes: Within a preset time period, the supercell is heated to 7000K and then cooled to room temperature to obtain the amorphous silicon dioxide.
3. The molecular modeling method for low-permeability complex porous media according to claim 1, characterized in that, After performing simulated annealing of the supercell according to preset heating and cooling parameters to obtain amorphous silicon dioxide, the process includes: The radial distribution functions of silicon-oxygen bonds, silicon-silicon bonds, and oxygen-oxygen bonds in the amorphous silicon dioxide were calculated to obtain the corresponding radial distribution simulation parameters. If the difference between the simulated radial distribution parameter and the corresponding experimental radial distribution value is within a preset deviation range, the step of performing three-dimensional spatial stretching and expansion on the amorphous silica system according to the overall porosity is executed.
4. The molecular modeling method for low-permeability complex porous media according to claim 3, characterized in that, The step of calculating the radial distribution functions of silicon-oxygen bonds, silicon-silicon bonds, and oxygen-oxygen bonds in the amorphous silicon dioxide to obtain the corresponding radial distribution simulation parameters includes: If the difference between the simulated radial distribution parameter and the corresponding experimental radial distribution value exceeds the deviation range, amorphous silicon dioxide is obtained again.
5. The molecular modeling method for low-permeability complex porous media according to claim 1, characterized in that, The steps of performing three-dimensional spatial stretching and expansion on the amorphous silica system according to its overall porosity to obtain a porous medium model include: The size of the amorphous silica system box is dynamically adjusted so that the overall porosity of the system reaches the preset target value; Annealing simulation was performed on the amorphous silica whose overall porosity reached the preset target value, which caused the silicon-oxygen framework to rearrange, resulting in a porous medium model whose overall porosity reached the preset target value and whose framework was stable.
6. The molecular modeling method for low-permeability complex porous media according to claim 1, characterized in that, After performing a three-dimensional spatial stretching and expansion of the amorphous silica system according to its overall porosity to obtain a porous medium model, the following steps are included: The porosity, pore surface area, average pore diameter, and connectivity parameters of the porous medium model are used to perform model standardization testing. If the test is passed, the number of hydroxyl groups to be added is obtained based on the pore surface area of the porous medium model. Hydroxyl groups are then added to the silicon atoms of the porous medium model to perform surface end capping, thereby obtaining the first porous medium model.
7. The molecular modeling method for low-permeability complex porous media according to claim 6, characterized in that, After performing model standardization testing based on the porosity, pore surface area, average pore diameter, and connectivity parameters of the porous media model, the following steps are included: If it fails, obtain a new porous media model.
8. The molecular modeling method for low-permeability complex porous media according to claim 6, characterized in that, The porosity, pore surface area, average pore diameter, and connectivity parameters of the porous media model were obtained by analyzing the pore structure parameters of the porous media model using Poreblazer software.
9. The molecular modeling method for low-permeability complex porous media according to claim 1, characterized in that, The preset deviation range is ±5%.
10. The molecular modeling method for low-permeability complex porous media according to claim 1, characterized in that, The gas molecules include CH4 and CO2.