A theoretical prediction method for sulfur solubility in acid-base mixed gases
Through molecular dynamics simulation and calculation methods, the problem of difficult prediction of elemental sulfur solubility in high-sulfur natural gas was solved, an accurate solubility prediction model was provided, the risk of sulfur deposition was reduced, and the normal production of gas wells was ensured.
Patent Information
- Application Number
- CN202311104088.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-30
- Publication Date
- 2025-10-03
- Estimated Expiration
- 2043-08-30
AI Technical Summary
Existing technologies make it difficult to accurately predict the solubility of elemental sulfur in high-sulfur natural gas, leading to sulfur deposition problems, affecting gas well productivity and posing safety risks.
The dissolution process of elemental sulfur in acid-base mixed gases was studied through molecular dynamics simulation. Combining first principles and machine learning methods, a solubility calculation model was established to predict the evolution law and thermodynamic kinetic process of sulfur solubility.
It has achieved accurate prediction of the solubility of elemental sulfur under different temperature and pressure conditions, provided a theoretical basis for the formation of sulfur deposition, and reduced the risk of sulfur deposition.
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Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of gas reservoir development, and more particularly to a theoretical prediction method for sulfur solubility in acid-base mixed gas. Background Art
[0002] As a clean, efficient, and low-carbon fossil energy source, natural gas shoulders the crucial mission of transitioning energy consumption from fossil fuels to renewable energy. During the development of high-sulfur natural gas, as formation temperature and pressure decrease, elemental sulfur dissolved in the natural gas precipitates, forming liquid sulfur or solid particles. When this sulfur reaches a certain mass, exceeds the fluid's carrying capacity, or is strongly adsorbed by throats or pipeline surfaces, it deposits in the formation and pipelines. Sulfur deposition not only clogs the formation, reduces formation permeability, and severely impacts gas well productivity, but can also completely block the well, threatening normal production, gathering, and management, and even causing serious industrial safety accidents. The essence of sulfur deposition is the reduced solubility of elemental sulfur in natural gas, which leads to the precipitation, growth, and agglomeration of condensed phases. Currently, research methods for sulfur solubility in sour gas reservoirs fall into two main categories: experimental and computational.
[0003] Due to the flammability and explosiveness of natural gas and the toxicity of some components in the gas mixture, experimental research is extremely difficult and dangerous. While computational simulation of sulfur deposition behavior doesn't involve safety issues, it's also extremely challenging. Understanding the microscopic mechanisms of sulfur solubility in highly sulfur-rich gases is the technical foundation for understanding and addressing sulfur deposition.
[0004] Computational methods mainly include three categories: (semi-)empirical models and thermodynamic models, machine learning algorithms, and molecular simulation.
[0005] The existing technology has the following disadvantages:
[0006] (1) First-principles calculation method combined with the equation of state: This method cannot fully characterize the solubility, and the amount of calculation is extremely large and the calculation accuracy requirements are extremely high.
[0007] (2) Molecular dynamics combined with Einstein crystal theory: Solubility is predicted by calculating the chemical formula of the dissolution equilibrium solid-liquid phase. However, the calculated value differs from the experimental value by 1-2 orders of magnitude, and the error mainly comes from the calculation of the solvation free energy.
[0008] (3) Classical molecular dynamics simulation: The diffusion process is simulated by placing solute molecules in an aggregated state. However, since it is impossible to reasonably determine the final dissolution state of the two-phase equilibrium, the reliability of the simulation results is questionable. Summary of the Invention
[0009] The purpose of the present invention is to solve the above-mentioned problems and provide a method for calculating and predicting the solubility of sulfur in acid-base mixed gases. The above technical purpose of the present invention is to study the interaction between elemental sulfur in sour natural gas and the main components of natural gas by molecular dynamics simulation of the "dissolution-condensation" process of mixed systems with different molecular ratios under different temperature and pressure conditions, and to obtain the molecular mechanism and physicochemical mechanism of the dissolution process. The method is applied to study the evolution of elemental sulfur solubility in different acid-base mixture systems with composition, temperature, and pressure; by simulating the nucleation and growth process of sulfur molecular clusters in natural gas and on the solid surface of rock formations, the main technical parameters of the thermodynamic and kinetic processes of the gas-liquid-solid conversion process are obtained. The technical effect of the present invention is to obtain the numerical value of elemental sulfur solubility in acid-base mixed systems through computational simulation and predict the formation of sulfur deposits.
[0010] The above technical objectives of the present invention are achieved through the following technical solutions: a theoretical prediction method for sulfur solubility in acid-base mixed gas, the method comprising the following steps:
[0011] S1: Force field optimization; for H2S, CO2, CH4 and S8, select and optimize the applicable force field parameters and verify the reliability of the force field;
[0012] S2: System testing: Through the system testing process, the appropriate supersaturation range of a mixed system is found to ensure the accuracy of the molecular dynamics simulation;
[0013] S3: Dissolution criterion: Based on the interactions between solute-solvent and solute-solute molecules, establish the criteria for identifying dissolved sulfur and solid sulfur.
[0014] S4: Solubility calculation: Calculate the mixed system selected in S2 and use the method in S3 to obtain the influence of various components on sulfur solubility.
[0015] The present invention is further configured as follows: S1 specifically includes the following steps:
[0016] S11: Use first principles and high-precision quantum chemical calculation methods to calculate the ground state energy of H2S, CO2, CH4 and S8 molecules under different interaction modes and distances;
[0017] S12: Using machine learning methods such as artificial neural networks, the force field parameters of the above molecules are established based on the data set obtained in S11.
[0018] S13: Verify the reliability of the force field.
[0019] The present invention is further configured as follows: S2 specifically includes the following steps:
[0020] S21: Establish an initial model for a mixture of solvent molecules and sulfur molecules at a range of concentration ratios, ensuring that the sulfur molecules are distributed as evenly as possible in the solvent. Set up an isothermal and isobaric ensemble to simulate the "dissolution-aggregation" process, where the sulfur molecules are fully dissolved before aggregating into clusters.
[0021] S22: Design different solute / solvent molecular ratios according to different supersaturations, obtain the relationship between the solubility prediction value and the supersaturation setting, and find the optimal supersaturation for sulfur solubility prediction;
[0022] S23: Based on the optimal supersaturation, design solute-solvent systems with different total number of molecules at this concentration to obtain the minimum system size to obtain stable and reliable solubility prediction values.
[0023] The present invention is further configured as follows: S3 specifically includes the following steps:
[0024] S31: Based on the intermolecular distance of sulfur molecules, the intermolecular distances under a series of temperatures and pressures were analyzed to obtain the intermolecular distances when clusters were formed;
[0025] S32: Based on S31, the partial embedding effect of solvent molecules is taken into account to obtain the modified intermolecular distance in the sulfur cluster;
[0026] S33: Based on S32, the formation criterion of sulfur clusters is obtained. Considering that the clusters themselves are surrounded by solvent, the number of clusters is counted as the number of dissolved sulfur molecules, and the number of molecules inside the clusters is counted as the number of solid sulfur molecules.
[0027] The present invention is further configured as follows: S4 specifically includes the following steps:
[0028] S41: After the system reaches equilibrium, data collection begins. The molecular trajectory distribution is used to perform cluster analysis of sulfur molecules. The number of dissolved sulfur molecules N is obtained using the method in S3. S , the number of solvent molecules is recorded as N X , then the solubility S can be expressed as the ratio of the two, that is, N S / N X .
[0029] S42: Change the composition of the mixed gas, draw a trend chart of the solubility change with the composition, and obtain the change of sulfur solubility under different components and concentrations.
[0030] S43: Change the temperature and pressure, draw a trend chart of solubility as a function of temperature and pressure, and obtain the effect of temperature and pressure changes on sulfur solubility.
[0031] In summary, the present invention has the following beneficial effects:
[0032] (1) A new model for the dissolution of elemental sulfur in sour natural gas based on the microscopic scale
[0033] Most literature and solubility prediction models assume that the solute is S8 and the solvent is an acidic gas such as H2S. A chemical reaction occurs between the solute and the solvent to form a new complex (such as open-chain H2S N ). However, whether the dissolution process of elemental sulfur in sulfur-bearing gas reservoirs is chemical dissolution or physical dissolution, or both, is itself an unresolved issue. The dissolution process of substances involves basic issues such as solute, solvent, and the interaction between solute and solvent. This technology uses first principles and ab initio molecular dynamics methods to study S N The interaction mode with molecules such as H2S and the stable structure of the complex are characterized at the atomic and molecular scales, as well as the chemical formula and complex structure of the solute molecules, and their changes with temperature and pressure, to verify the traditional dissolution mechanism.
[0034] (2) A new molecular simulation method for predicting the solubility of elemental sulfur in sour natural gas
[0035] Due to the complexity of sour natural gas systems, traditional molecular solubility simulation methods have shown deficiencies in reliability, reproducibility, and computational efficiency. This technology, based on a molecular dynamics approach to solute processes in supersaturated solutions, calculates the solubility value by calculating the number of unaggregated sulfur molecules and the number of formed sulfur clusters.
[0036] (3) New method for predicting the precipitation ability of elemental sulfur on solid surfaces
[0037] The precipitation of sulfur molecules from sour natural gas undergoes a series of processes, including aggregation, nucleation, growth, and phase transition. Before and after elemental sulfur nucleation, adsorption occurs at the throat or pipeline interface. The smaller the sulfur molecule aggregates (clusters), the higher their surface activity and the greater their likelihood of adsorption. However, traditional sulfur deposition theory holds that solid sulfur will only deposit when its mass becomes too large for the fluid to support. This technology combines first-principles calculations with experimental characterization to determine the thermodynamic and kinetic mechanisms for the aggregation and precipitation of sulfur molecules on solid surfaces, proposing a new mechanism for sulfur precipitation and providing new evidence for sulfur deposition theory. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Figure 1 It is a technical roadmap for the implementation scheme in the embodiment of the present invention;
[0039] Figure S2-1 This is a curve showing the change of solubility with the ratio of the number of S8 to H2S molecules in the embodiment of the present invention;
[0040] Figure S2-2 This is a curve showing the change of solubility with the ratio of the number of H2S molecules at a fixed ratio in an embodiment of the present invention;
[0041] Figure S3-1 The distribution of the distance between S8 molecules at different simulation times under the conditions of T = 363.2K, P = 36.21Mpa in the embodiment of the present invention;
[0042] Figure S4-1 The calculated and experimental values of the solubility of S8 in H2S in the embodiment of the present invention are compared;
[0043] Figure S4-2 8 is the solubility of S8 in H2S as a function of pressure (a) and temperature (b) in the embodiment of the present invention. DETAILED DESCRIPTION
[0044] In order to enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of 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 the present invention.
[0045] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the embodiments.
[0046] Example:
[0047] like Figure 1 As shown, a theoretical prediction method for the solubility of sulfur in an acid-base mixed gas is provided. This embodiment takes the prediction of the solubility of sulfur in H2S as an example and includes the following steps:
[0048] S1: Force field optimization:
[0049] For H2S, we used the force field proposed by Galliero et al., treating H2S molecules as uncharged interaction points. This force field was used to calculate the density of H2S at temperatures between 363.2 and 403.2 K and pressures between 20 and 60 MPa. The calculated values agree well with the experimental values, with relatively small relative errors (mostly less than 6%). See Table S1-1 for details.
[0050] Table S1-1: Comparison of simulated and experimental values of H2S density
[0051]
[0052] The force field used for S8 was calculated by Ballone et al. based on density functional theory. The lattice parameters of a supercell containing 456 S8 molecules at a temperature of 298 K and a pressure of 0.1 MPa were calculated based on this. The relative error between the calculated and experimental values was less than 3%, demonstrating the accuracy of the force field. See Table S1-2 for details.
[0053] Table S1-2: Comparison of simulated and experimental values of S8 lattice parameters
[0054]
[0055] S2: System test:
[0056] In each test system, the number of H2S molecules is 10,000, and then 250, 300, 350, 400, 450, and 500 S8 molecules are added to the H2S system respectively. Molecular dynamics (MD) simulations are performed on each system and the solubility is calculated. The results show that the sulfur solubility increases with the increase of N S8 / N H2S The change curve of
[0057] like Figure S2-1 As shown in the figure, the results show that the solubility reaches a stable value when the ratio of S8 to H2S is greater than 1 / 25; when the ratio of 1 / 20 is kept unchanged, the solubility of the system with 5000, 10000, 15000 and 20000 H2S molecules is calculated respectively. The results show that the solubility tends to be stable after the number of H2S molecules is greater than 10000. Figure S2-2 shown.
[0058] Therefore, a mixed system of 20,000 H2S and 1,000 S8 was selected for subsequent calculations.
[0059] S3: Dissolution criterion:
[0060] In a supersaturated system, S8 molecules will gradually aggregate and form clusters. As the simulation time increases, the size of the S8 clusters continues to increase until the system reaches aggregation-dissolution equilibrium, at which point their number and size tend to remain constant. In equilibrium, S8 is dispersed in the box in the form of clusters and molecules of varying sizes. The solubility can be determined by the number of dissolved S8 molecules. The cutoff threshold (R cut) is determined by the distance between S8 molecules. The distance between S8 molecules is defined as the closest distance between any two S atoms in different S8 molecules. Since the distance between S8 molecules is usually within a wide range and varies with temperature and pressure, a 6ns molecular dynamics simulation was performed on an amorphous pure S8 system (containing 1000 S8 molecules) with a temperature and pressure range of 363.2-393.2K and 30-50MPa in the NPT ensemble. 20 frames of 1ns of equilibrium data were taken to analyze the distribution of the distance between S8 molecules, see Figure S3-1 As shown in the figure, the analysis indicates that the closest distance between any two S atoms in different S8 molecules is within the range of 0.343-0.344 nm. For ease of comparison, the cutoff threshold for determining sulfur cluster formation in subsequent solubility calculations was set at 0.344 nm.
[0061] S4: Solubility calculation:
[0062] In the MD simulation, the conjugate gradient algorithm is first used to minimize the energy of the initial structure, and then the dissolution process of S8 in H2S is simulated in an isothermal isobaric ensemble (NPT), where the temperature and pressure are controlled by the Nosé-Hoover thermostat and barostat, respectively. Periodic boundary conditions are applied in three directions. The cut-off radius of the van der Waals interaction is taken as 1.4nm. The Verlet algorithm is used to solve the Newtonian equations of motion with a time step of 1.0fs. The simulation process usually takes 10-15ns to reach an equilibrium state where the relative standard deviation of energy is less than 1% and the dissolved S8 molecules tend to be constant. An additional 5ns of simulation is then performed for data analysis. The solubility of elemental sulfur in pure H2S is predicted under the conditions of T = 353.2-403.2K and P = 30-60MPa, and the effects of temperature and pressure on the solubility are discussed. See Figure S4-1 .
[0063] Since the formed clusters occupy the space of at least one S8 molecule surrounded by solvent molecules, each sulfur cluster is also considered as a dissolved S8 molecule. Therefore, the total number of dissolved S8 molecules is the sum of the number of S8 molecules dispersed in the system and the number of formed S8 clusters. Since the number of H2S molecules occupying the cluster surface is much smaller than the total number of solvent molecules, this approximation has little effect on the solubility calculation. The solubility of elemental sulfur (S cal ) is defined as S cal =N S / N sol Where N S is the number of dissolved S8 molecules, N solis the number of solvent H2S molecules. In a typical MD simulation, the number of S8 clusters initially increases with simulation time and then approaches a constant value at equilibrium. Consequently, the number of dissolved S8 molecules initially decreases with time, ultimately reaching a stable value around 10-15 ns.
[0064] As shown in Table S4-1 and Figure S4-2 As shown in the figure, calculations show that at a certain temperature, the solubility increases with increasing pressure. This dissolution process apparently involves a reduction in the volume of the S8 molecule surrounded by numerous H2S molecules, a process favored by high pressure. Density functional theory and Hartree-Fock calculations of the structure and energy of the H2S-S8 complex indicate a weak interaction between the H2S and S8 molecules, with a binding energy of approximately 3 kcal / mol. This is a typical physical interaction that is insensitive to temperature changes. Therefore, the solubility of this system varies little with temperature.
[0065] Table S4-1: Density of mixed systems and S8 solubility under different temperature and pressure conditions
[0066]
[0067]
[0068] This specific embodiment is merely an explanation of the present invention and is not intended to limit the present invention. After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed. However, as long as such modifications are within the scope of the claims of the present invention, they are protected by patent law.
Claims
1. A theoretical prediction method for sulfur solubility in acid-base mixed gas, characterized by: The method comprises the following steps: S1: Force field optimization: For H2S, CO2, CH4 and S8, select and optimize the applicable force field parameters and verify the reliability of the force field; S2: System testing: Through the system testing process, the appropriate supersaturation range of a mixed system is found to ensure the accuracy of the molecular dynamics simulation; S3: Dissolution criterion: Based on the solute-solvent and solute-solute molecular interactions, establish the criterion for identifying dissolved sulfur and solid sulfur; S4: Solubility calculation: Calculate the mixed system selected in S2 and use the method in S3 to obtain the effect of various components on sulfur solubility; The S1 specifically includes the following steps: S11: Calculate the ground state energies of H2S, CO2, CH4 and S8 molecules under different interaction modes and distances using first-principles and high-precision quantum chemical calculation methods; S12: Using artificial neural network machine learning methods, the force field parameters of the above molecules are established based on the data set obtained in S11; S13: Verify the reliability of the force field; The S2 specifically includes the following steps: S21: Establish an initial model for a mixture of solvent molecules and sulfur molecules at a range of concentration ratios, ensuring that the sulfur molecules are distributed as evenly as possible in the solvent. Set up an isothermal and isobaric ensemble to simulate the "dissolution-aggregation" process, where the sulfur molecules are fully dissolved before aggregating into clusters. S22: Design different solute / solvent molecular ratios according to different supersaturations, obtain the relationship between the solubility prediction value and the supersaturation setting, and find the optimal supersaturation for sulfur solubility prediction; S23: Based on the optimal supersaturation, design solute-solvent systems with different total number of molecules at this concentration to obtain the minimum system size that can achieve stable and reliable solubility prediction values; The S3 specifically includes the following steps: S31: Based on the intermolecular distance of sulfur molecules, the intermolecular distances under a series of temperatures and pressures were analyzed to obtain the intermolecular distances when clusters were formed; S32: Based on S31, the partial embedding effect of solvent molecules is considered to obtain the modified intermolecular distance in the sulfur cluster; S33: Based on S32, the formation criterion of sulfur clusters is obtained. Considering that the clusters themselves are surrounded by solvent, the number of clusters is counted as the number of dissolved sulfur molecules, and the number of molecules inside the clusters is counted as the number of solid sulfur molecules; The S4 specifically includes the following steps: S41: After the system reaches equilibrium, data collection begins. The molecular trajectory distribution is used to perform cluster analysis of sulfur molecules. The number of dissolved sulfur molecules N is obtained using the method in S3. S , the number of solvent molecules is recorded as N X , then the solubility S can be expressed as the ratio of the two, that is, N S / N X ; S42: Change the composition of the mixed gas and draw a trend chart of solubility versus composition to obtain the change in sulfur solubility under different components and concentrations; S43: Change the temperature and pressure, draw a trend chart of solubility as a function of temperature and pressure, and obtain the effect of temperature and pressure changes on sulfur solubility.