Saline water layer CO2 storage pressure control method based on molecular simulation and density-depth chart construction method
By constructing a multi-component formation water molecule model and long-term molecular dynamics simulation, combined with a temperature-pressure coupling model, the problem of large density prediction error in CO2 sequestration of deep saline water layers was solved, realizing high-precision density prediction and visualization of CO2 sequestration, and simplifying engineering design.
Patent Information
- Application Number
- CN202511024975.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-24
- Publication Date
- 2025-11-14
AI Technical Summary
Traditional models for CO2 sequestration in deep saline aquifers suffer from large density prediction errors, idealized simulation parameters, and insufficient simulation duration, failing to meet the requirements for high-precision parameters, especially in areas with abnormally high pressure where it is difficult to obtain equilibrium density data.
By constructing a multi-component formation water molecule model, employing geometric optimization and long-term molecular dynamics simulation, and combining a temperature-pressure coupling model, equilibrium density data was obtained. The CO2 density was calculated using the Span-Wagner equation of state, and a layered structure was established for pressure-controlled molecular dynamics simulation.
It significantly improves the accuracy of deep saline aquifer density prediction, reduces costs, provides microscopic evidence and visualizations for CO2 sequestration, and simplifies the engineering design process.
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Figure CN120954526A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method for controlling the pressure of CO2 sequestration in saline aquifers based on molecular simulation and a method for constructing density-depth maps. It belongs to the field of CO2 geological sequestration technology and is applicable to the assessment of CO2 sequestration potential in deep saline aquifers and the study of formation fluid dynamics. It provides a model for future research on molecular simulation of diffusion of different gases in saline aquifers. Background Technology
[0002] The density of saline aquifer water is a key parameter for calculating the dissolution and sequestration rate and sequestration volume during CO2 geological sequestration. Traditional experiments struggle to simulate the abnormally high pressure conditions of deep saline aquifer water. Abnormally high pressure is commonly found in deep saline aquifer water (>4000m) in the central Junggar Basin, resulting in density formulas that differ from those used in atmospheric pressure zones (D<4000m). Furthermore, the complex ionic composition of saline aquifer water (such as Na+) contributes to its high density. + K + Ca 2+ HCO -3 The effect of fluid density is difficult to accurately characterize using simplified models.
[0003] Furthermore, existing models are mostly based on simplified calculations using single-ion solutions (such as NaCl), neglecting the complex ionic composition (such as Ca) in the saline formation water of the Junggar Basin. 2+ Mg 2+ HCO -3 The synergistic effect on fluid density. This simplification is applicable under low salinity conditions, but when dealing with high salinity formation water in the Sangonghe Formation of the Mahu Depression, the density calculation deviation is large, which seriously affects the reliability of the assessment of CO2 dissolved and stored capacity.
[0004] While existing molecular dynamics simulation techniques can characterize the behavior of multi-component fluids, their application in deep saline formation water still has limitations: First, simulation parameters (such as temperature and pressure boundary conditions) often employ idealized assumptions and are not deeply coupled with actual regional temperature and pressure conditions; second, simulation duration is insufficient, making it difficult to obtain equilibrium density data, especially in abnormally high-pressure sections (D>4000m) where insufficient system relaxation can lead to density fluctuations. These limitations fail to meet the high-precision parameter requirements of deep saline formation water CO2 sequestration engineering practices.
[0005] The density of CO2 varies significantly under different temperatures and pressures, making it difficult for traditional isothermal and isobaric ensembles to bring the CO2 AC model to molecular dynamics equilibrium within a short time. Therefore, this invention is proposed. Summary of the Invention
[0006] The purpose of this invention is to provide a molecular simulation-based method for controlling the pressure of CO2 sequestration in saline aquifers and a method for constructing density-depth maps. This method overcomes the limitations of traditional models in density prediction in abnormally high-pressure sections and provides new ideas and visualization maps for the study of the microscopic behavior of CO2 geological sequestration and other gases.
[0007] The present invention provides a method for controlling the pressure of CO2 sequestration in saline aquifers based on molecular simulation and a method for constructing density-depth maps, comprising the following steps:
[0008] S1. Collect geothermal gradient and pressure gradient data of typical wells in the central part of the Junggar Basin;
[0009] S2. Based on temperature and pressure data, construct a coupled model of depth with temperature and pressure;
[0010] S3. Construct a multi-component formation water molecule model and perform geometric optimization;
[0011] S4. Perform molecular dynamics simulations under selected temperature and pressure conditions and extract equilibrium density data;
[0012] S5. Based on the simulation results, fit the empirical formula for saline aquifer formation water density-depth.
[0013] S6. Calculate CO2 density based on the Span-Wagner equation of state, construct a CO2 molecular model and perform geometric optimization;
[0014] S7. Establish the stratified structure of CO2 and saline formation water, and conduct pressure-controlled molecular dynamics simulations.
[0015] Preferably, in step S1, the geothermal gradient is 11.6–26.5℃ / km, with an average of 20.2℃ / km. When the depth D < 4000m, the pressure gradient is 10.496–11.431MPa / km, with an average of 10.941MPa / km. When the depth D > 4000m, it is an abnormally high-pressure section.
[0016] Preferably, in step S2, the coupling models of depth with temperature and pressure are shown in equations (1) and (2), respectively:
[0017] T = 0.0202D + 288.15 (1)
[0018]
[0019] In the formula, T is the formation temperature (K); P is the formation pressure (MPa); and D is the formation depth (m).
[0020] Preferably, in step S3, a multi-component formation water molecular model is configured with 12,700 H2O molecules, 90 NaCl molecules, 5 KCl molecules, 3 NaHCO3 molecules, 1 CaCl2 molecule, and 1 MgCl2 molecule in a specific ratio. An initial saline formation water model is generated using the Amorphous Cell module of MaterialStudio software. After geometric optimization, a stable saline formation water model is obtained, which accurately simulates the ion synergistic effect of highly saline formation water.
[0021] Preferably, in step S3, the geometric optimization conditions for the saline formation water model are: COMPASS III force field, cutoff radius... Energy convergence threshold 2×10 -5 ~2×10 -3 kcal / mol, pressure convergence threshold 1×10 -3 ~2.5GPa.
[0022] Preferably, in step S4, the conditions for the molecular dynamics simulation include:
[0023] The NPT ensemble was used; the simulation duration was 400 ps; the temperature control method was the Nose method, and the pressure control method was the Berendsen method; the time step was 1 fs; the cutoff radius was 9.5–18.5 Å; electrostatic interactions were handled using the Ewald summation method with an accuracy of 1 × 10⁻⁶. -5 ~1×10 -2 kcal / mol.
[0024] Preferably, in step S4, 10 temperature and pressure points are selected within a depth range of 200-5000m to perform 400ps molecular dynamics simulations to obtain the equilibrium density data of the saline aquifer formation water.
[0025] Preferably, in step S5, the linear relationship between the formation water density ρ of the saline aquifer and the depth D is shown in equation (3):
[0026]
[0027] In the formula, ρ is the density, in g / cm³. 3 D represents the depth of the formation, in meters (m).
[0028] Preferably, in step S7, the pressure control simulation of the layered structure includes the following steps:
[0029] The optimized saline formation water model was cut along the (001) crystal plane and a unit cell was constructed. The unit cell parameters of the optimized CO2 molecular model were made consistent with the unit cell parameters of the saline formation to establish a layered structure. Molecular dynamics simulation was performed using the NVT ensemble with a total duration of 100 ps, a time step of 1.0 fs, and a total number of steps of 1 × 10⁻⁶.5 Each step outputs one frame of trajectory file every 5000 steps.
[0030] Preferably, the method for generating a saline formation water density map is as follows: using depth as the vertical axis and density as the horizontal axis, a visual map that can be directly used for engineering design is formed.
[0031] Preferably, cross-scale data fusion: connecting molecular-scale simulation results with macroscopic geological models to bridge the gap between microscopic mechanisms and engineering applications, providing a new paradigm for the study of formation water in deep saline aquifers.
[0032] This invention effectively captures the ion synergistic effect of highly saline aquifers by constructing a multi-component formation water molecule model and employing geometric optimization. This overcomes the bias of traditional simplified models under high-salinity conditions in the Mahu Depression of the Junggar Basin, reducing density prediction errors to an engineering-acceptable range.
[0033] This invention fits a piecewise linear relationship based on simulation results, significantly improving the accuracy of deep saline aquifer density prediction (see...). Figure 13 and Figure 14 The fitted scatter plot, especially in abnormally high pressure sections (such as pressure of 114.47 MPa at a depth of 5000m), traditional methods are prone to fluctuations due to insufficient relaxation, while the present invention ensures data stability through long-term simulation (400ps).
[0034] This invention calculates CO2 density using the Span-Wagner equation of state and constructs a layered structural model (CO2 and formation water in the saline aquifer), performing pressure-controlled molecular dynamics simulations (100 ps) under the NVT ensemble. This visualizes the diffusion behavior of CO2 in the saline aquifer, providing a microscopic basis for pressure-controlled operations. For example, under conditions of 368.95 K and 100 MPa, the model demonstrates the interfacial behavior between CO2 and formation water, which helps optimize the design of storage pressure to reduce leakage risks.
[0035] The visualization charts generated by this invention can be directly used in CO2 sequestration engineering design, simplifying the parameter evaluation process. Compared with traditional experimental methods, simulation time is reduced by more than 50% (equilibrium data can be obtained in 400 ps simulation), and high-pressure experimental equipment is not required, thus reducing costs. Simultaneously, the cross-scale fusion technology (interfacing microscopic molecular models with macroscopic geological data) provides a reusable template for similar gas sequestration studies (such as CH4 or H2). Attached Figure Description
[0036] Figure 1 This is a flowchart of the method for controlling the pressure of CO2 sequestration in saline aquifers based on molecular simulation and the method for constructing density-depth maps according to the present invention;
[0037] Figure 2This is a density diagram for a simulation duration of 400 ps, a temperature of 304.31 K, and a pressure of 10.79 MPa in an embodiment of the present invention.
[0038] Figure 3 This is a density diagram for a simulation duration of 400 ps, a temperature of 320.47 K, and a pressure of 19.54 MPa in an embodiment of the present invention.
[0039] Figure 4 This is a density diagram for a simulation duration of 400 ps, a temperature of 336.63 K, and a pressure of 28.29 MPa in an embodiment of the present invention.
[0040] Figure 5 This is a density diagram for a simulation duration of 400 ps, a temperature of 352.79 K, and a pressure of 37.04 MPa in an embodiment of the present invention.
[0041] Figure 6 This is a density diagram for a simulation duration of 400 ps, a temperature of 368.95 K, and a pressure of 45.80 MPa in an embodiment of the present invention.
[0042] Figure 7 This is a density diagram for a simulation duration of 400 ps, a temperature of 372.99 K, and a pressure of 93.06 MPa in an embodiment of the present invention.
[0043] Figure 8 This is a density diagram for a simulation duration of 400 ps, a temperature of 377.03 K, and a pressure of 99.73 MPa in an embodiment of the present invention.
[0044] Figure 9 This is a density diagram for a simulation duration of 400 ps, a temperature of 381.07 K, and a pressure of 105.27 MPa in an embodiment of the present invention.
[0045] Figure 10 This is a density diagram for a simulation duration of 400 ps, a temperature of 385.11 K, and a pressure of 110.11 MPa in an embodiment of the present invention.
[0046] Figure 11 This is a density diagram for a simulation duration of 400 ps, a temperature of 389.15 K, and a pressure of 114.47 MPa in an embodiment of the present invention.
[0047] Figure 12 This is a scatter plot of formation pressure versus depth according to an embodiment of the present invention;
[0048] Figure 13 Figure 1 shows a scatter plot of formation water density and depth in a saline aquifer according to an embodiment of the present invention; Figure 2a represents a scatter plot of formation water density and depth in a saline aquifer at normal pressure, and Figure 3b represents a scatter plot of formation water density and depth in a saline aquifer at high pressure.
[0049] Figure 14The following are CO2 relationship diagrams for embodiments of the present invention: Figure a represents the relationship between CO2 density and pressure; Figure b represents the relationship between CO2 density and temperature; Figure c represents the relationship between CO2 density and depth at normal pressure; Figure d represents the relationship between CO2 density and depth at abnormally high pressure.
[0050] Figure 15 This is a molecular dynamics simulation diagram of the Layer model at 100 ps according to an embodiment of the present invention;
[0051] Figure 16 Figure 1 shows the adsorption capacity, heat of adsorption, and interaction energy of CO2 under temperature-pressure coupling in an embodiment of the present invention; Figure 2a represents the depth-adsorption curve; Figure 2b represents the depth-heat of adsorption curve; Figure 2c represents the depth-interaction energy curve. Detailed Implementation
[0052] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.
[0053] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.
[0054] Example 1
[0055] The present invention provides a method for controlling the pressure of CO2 sequestration in saline aquifers based on molecular simulation and a method for constructing density-depth maps, the process of which is as follows: Figure 1 As shown, the specific steps include the following:
[0056] Step S1: Regional geological data acquisition and verification.
[0057] Regional surface temperature data were obtained from the literature "Simulation Analysis of the Middle Section of the Southern Margin of the Junggar Basin" and regional geothermal gradient data were obtained from the literature "Analysis of CO2 Geological Sequestration Potential of Oil and Gas Basins in my country". The surface temperature of the Junggar Basin was determined to be 15℃, the geothermal gradient was 11.6~26.5℃ / km, and the average was 20.2℃ / km.
[0058] Step S2: Formation pressure segmentation modeling.
[0059] Based on measured pressure data from the literature "Characteristics and Controlling Factors of Formation Pressure Anomalies in the Central Junggar Basin," accurate data points were extracted using a data point sampling tool. A scatter plot was then created using these data points, and fitting formulas were developed for the normal pressure zone and the abnormally high pressure zone. The normal pressure zone (D≤4000m) and the abnormally high pressure zone (D>4000m) were defined. The pressure gradient in the normal pressure zone ranges from 10.496 to 11.431 MPa / km, with an average of 10.941 MPa / km. The pressure in the abnormally high pressure zone was calculated using the fitting formulas.
[0060] The temperature-pressure coupling and CO2 density data calculated in this embodiment are shown in Table 1.
[0061] Table 1. Temperature-pressure coupling and CO2 density data
[0062]
[0063] Figure 12 The scatter plot of formation pressure versus depth in this embodiment shows that the high-pressure anomaly in the work area occurs at a depth of approximately 4000m. In the normal pressure zone, the formation pressure ranges from 2.02 to 45.80 MPa, while in the high-pressure zone, the formation pressure ranges from 45.80 to 114.47 MPa.
[0064] Step S3: Construction of the temperature-pressure coupling equation.
[0065] Establish a dynamic coupling model between depth and temperature and pressure:
[0066] Temperature model:
[0067] T = 0.0202D + 288.15 (1)
[0068] Stress model:
[0069]
[0070] In the formula, T is the formation temperature (K); P is the formation pressure (MPa); and D is the formation depth (m).
[0071] Step S4: Configuration of formation water ion composition in the saline aquifer.
[0072] Based on the formation water chemical analysis results in "Study on Reservoir Characteristics and Genesis of Low-Stable Oil Layers in the Second Member of the Sangonghe Formation in the Mahu Depression of the Junggar Basin", 12,700 H2O molecules, 90 NaCl molecules, 5 KCl molecules, 3 NaHCO3 molecules, 1 CaCl2 molecule and 1 MgCl2 molecule were prepared in proportion to ensure that the ion mass percentage was consistent with the measured data.
[0073] The formation water ion mineralization used in this embodiment is shown in Table 2.
[0074] Table 2 Formation water ion mineralization
[0075] well name <![CDATA[K + +That + ]]> <![CDATA[Ca 2+ ]]> <![CDATA[Mg 2+ ]]> <![CDATA[Cl - ]]> <![CDATA[HCO3 - ]]> mineralization M16009 9681.9 279.9 107.3 15430 496.4 26015 M16025 9854 215.9 105.8 15518.9 604.5 26312.7 M16043 9879 198 139.3 15251.6 1250.2 26727.4 M16237 8659.2 222.2 95.9 13718.6 513.3 23214.3 M16017 11122.1 212 100.1 17465.2 586.1 29492.9 M16035 9774.9 209.6 117.2 15358.8 709 26182.6 M16053 10407.4 285 130.9 16717.2 354.5 27902.4 M16054 10116.3 283.7 137 16103.8 662.7 27312.6 M16075 9382.2 114.2 50.5 14077.5 1150.8 24863.9 M16253 11360.1 202.3 87.5 17586 914.4 30159.7 Ma041 10579.1 142.4 113.1 15938 1627.9 28407.8 Ma181 10707.1 200.2 83.4 16762.7 573.2 28335.1 average value 10126.9417 213.783333 105.666667 15827.3583 786.916667 27077.2
[0076] Table 3 shows a comparison of the percentage content of model ions and actual ions in this embodiment. It can be seen that Cl... - The error between the constructed ion percentage and the actual ion percentage is 2.7%, and the error between the constructed H2O molecule percentage and the actual molecule percentage is 0.04%, which is highly accurate and meets the experimental requirements.
[0077] Table 3 Comparison of the percentage content of model ions and actual ions in the examples.
[0078]
[0079] Step S5: Initial construction of the molecular model.
[0080] Using the Amorphous Cell module in Material Studio software, input the above ion ratio and set the initial density to 1 g / cm³. 3 Generate an initial amorphous saline aquifer formation water model.
[0081] Step S6, setting geometry optimization parameters.
[0082] Geometry optimization was performed using COMPASS III force fields, with the cutoff radius set to... The energy convergence threshold is 2 × 10 -5 kcal / mol, pressure convergence threshold is 1×10 -3 GPa.
[0083] Step S7, molecular dynamics pre-equilibrium.
[0084] A 50ps pre-balancing simulation was performed under the NPT ensemble, with the Nose method for temperature control and the Berendsen method for pressure control, and a time step of 1fs to ensure initial relaxation of the system.
[0085] Step S8: Long-term molecular dynamics simulation.
[0086] Ten temperature and pressure data points were selected for a depth range of 200-5000 m. In the normal pressure range, five temperature and pressure points were selected as simulation points. All temperature and pressure data points were selected for the abnormal pressure range. Molecular dynamics simulations were performed on the selected simulation points at 400 ps. The cutoff radius was [missing information]. Electrostatic effects were handled using the Ewald summation method with an accuracy of 1×10⁻⁶. -5 kcal / mol. Density plots at different temperatures and pressures are shown below. Figures 2-11 As shown, the 50ps molecular dynamics simulation was used to bring the saline aquifer water density to equilibrium. The 400ps simulation was used to collect density data. It can be observed that the density fluctuations are small, tending towards an average value.
[0087] Step S9, equilibrium density extraction.
[0088] The Forcite module was used to statistically analyze the density fluctuation over 400 psMD, and the average value was taken as the equilibrium density ρ.
[0089] Step S10: Piecewise linear regression analysis.
[0090] The least squares method was used to fit the data for the normal pressure range and the abnormal high pressure range, respectively:
[0091]
[0092] In the formula, ρ is the density (g / cm³). 3 ); D is the depth of the stratum (m).
[0093] The scatter plot of saline aquifer formation water density and depth in this embodiment is shown below. Figure 13 As shown, in the normal pressure range, the density of the saline water layer exhibits a good linear relationship with depth. The density decreases with increasing depth. In the high-pressure range, the density initially increases and then decreases with increasing depth. The change in saline water layer density with depth is essentially a result of the competition between pressure and temperature. In the shallow region, temperature dominates the density, and the density decreases uniformly with increasing depth. In the deep region, the pressure effect intensifies, and the density becomes pressure-dominated, subsequently continuing to be dominated by temperature. Overall, the density decreases uniformly with increasing depth, but the change is not significant.
[0094] Step S11: Calculate the CO2 density at different temperatures and pressures using the Span-Wagner equation of state through the NIST CO2 Fluid Properties website.
[0095] Step S12: After obtaining the CO2 density at different temperatures and pressures, construct an amorphous CO2 unit cell using an Amorphous Cell. Select the Construction task, set the initial CO2AC density based on the calculated CO2 densities at different temperatures and pressures, input 200 CO2 molecules, and handle electrostatic interactions using the Ewald summation method with a precision of 1×10⁻⁶. -4 kcal / mol, cutoff radius is Based on this, CO2AC models under different temperatures and pressures are constructed, providing a foundation for pressure control strategies.
[0096] Step S13: Establish a diffusion model through the Build Layer.
[0097] Taking 368.95 K and 100 MPa as an example, an amorphous unit cell was constructed using an Amorphous Cell. The Construction task was selected, and the initial AC density of the saline formation water was set according to the molecular dynamics simulation density. Electrostatic interactions were handled using the Ewald summation method, with an accuracy of 1 × 10⁻⁶. -4 kcal / mol, cutoff radius is Input 12700 H2O molecules, 90 NaCl molecules, 5 KCl molecules, 3 NaHCO3 molecules, 1 CaCl2 molecule, and 1 MgCl2 molecule proportionally. This generates a saline aquifer formation water AC model. Geometric optimization is then performed on the saline aquifer formation water AC model. The COMPASS III force field is used for geometric optimization, with the cutoff radius set to... The energy convergence threshold is 2 × 10 -5 kcal / mol, pressure convergence threshold of 0.001 GPa. Maximum number of iterations set to 5 × 10⁻⁶. 4 Step 1. Since the density of the saline formation water is already determined, there is no need to optimize the cell parameters. Perform a cleave surface operation on the obtained AC optimization model of the saline formation water. Since the (001) plane is the plane with the lowest energy, this plane is cleave surface. Then select Crystals-Build Vacuum slab to build a vacuum layer. Set the thickness to The saline aquifer crystal model of the (001) plane can then be obtained. The previously optimized CO2AC model at 368.95K and 100MPa is then subjected to the Build Nanostructure-Nanocluster operation. Values in the X, Y, and Z directions are input to ensure consistency between the cell parameters of the saline aquifer and the CO2AC model. After establishing the CO2 nanoclusters, the BuildCrystals operation is performed on them, inputting cell parameters to ensure consistency between the cell parameters of the saline aquifer and the CO2AC model. Then, the BuildLayer operation is performed. The created saline aquifer crystal model and CO2 crystal model are selected, and the BuildLayered structure as a crystal operation is performed to obtain the preliminary layer model.
[0098] Step S14: Perform geometric optimization on the established layer model.
[0099] Geometry optimization was performed using COMPASS III force fields, with the cutoff radius set to... The energy convergence threshold is 2 × 10 -5 kcal / mol, pressure convergence threshold of 0.001 GPa. Maximum number of iterations set to 5 × 10⁻⁶. 4 Step 1. Do not optimize cell parameters.
[0100] Step S15: Perform molecular dynamics simulation on the optimized layer model.
[0101] Select the NVT ensemble. Input the temperature parameters, with each kinetic step lasting 1.0 fs. The total number of kinetic simulation steps is 100,000, with one frame output to the trajectory file every 5,000 steps to record the system state during the simulation. The total duration of the kinetic simulation is 100 ps. Implement pressure control of CO2 in saline formation water.
[0102] Step S16, Visualization of the graphic design.
[0103] Plot a piecewise fitting curve with depth D as the ordinate and density ρ as the abscissa. This will create a chart that can be directly used in engineering design.
[0104] Figure 14 The diagrams illustrate the relationship between CO2 density and temperature, pressure, and depth in embodiments of this application. Figure a represents the relationship between CO2 density and pressure; figure b represents the relationship between CO2 density and temperature; figure c represents the relationship between CO2 density and depth at normal pressure; and figure d represents the relationship between CO2 density and depth at abnormally high pressure. It can be seen that CO2 density is positively correlated with pressure, and the change in CO2 density gradually decreases with increasing pressure, eventually leveling off. When the system temperature is above the critical temperature, as the CO2 pressure increases, the CO2 phase changes from gaseous to supercritical, and the CO2 density gradually increases, and this change is continuous; CO2 density is negatively correlated with temperature. Under a pressure of 45.8 MPa, as the temperature increases, the CO2 density continuously decreases due to volume expansion, and the CO2 state on the isobars changes from liquid to supercritical. At this point, no abrupt density change occurs with increasing temperature, and the higher the pressure, the smaller the change in CO2 density; in both the normal and high-pressure zones, CO2 density and depth are positively correlated. The change in CO2 density with depth is essentially a result of the competition between pressure (increasing density) and temperature (decreasing density). In shallow regions, density is dominated by pressure, and it increases rapidly with depth. In deeper regions, the temperature effect intensifies, offsetting the pressure effect, and the rate of increase in density slows significantly. Overall, density increases with depth, but the rate of increase gradually diminishes.
[0105] The molecular dynamics simulation diagram of the Layer model at 100 ps in this embodiment is as follows: Figure 15 As shown, CO2 initially diffuses into the saline formation water, providing a basis for the study of the CO2 permeation and diffusion mechanism in the saline aquifer under different temperature and pressure conditions.
[0106] In this embodiment, the adsorption capacity, heat of adsorption, and interaction energy of CO2 under thermo-baric coupling are as follows: Figure 16As shown in Figure a, in both the atmospheric and high-pressure sections, the CO2 adsorption capacity gradually increases with depth (due to simultaneous increases in temperature and pressure), indicating that pressure primarily dominates the adsorption capacity. Figure b shows that in the atmospheric pressure section, CO2 adsorption in the saline aquifer is physical adsorption (adsorption heat < 42 kJ / mol). With increasing depth, the adsorption heat generally decreases, indicating that the CO2 adsorption capacity gradually weakens with increasing depth. Essentially, this is a result of the competition between increased temperature (enhanced adsorption capacity) and increased pressure (weakened adsorption capacity), suggesting that within 4000 m, pressure has a greater impact on CO2 adsorption capacity. In the high-pressure section, with increasing depth, the adsorption heat generally increases, indicating that the CO2 adsorption capacity gradually strengthens with increasing depth. Essentially, this is a result of the competition between increased temperature (enhanced adsorption capacity) and increased pressure (weakened adsorption capacity), suggesting that within depths > 4000 m, temperature has a greater impact on CO2 adsorption capacity. As shown in Figure c, in the normal pressure range, the CO2 interaction energy gradually increases with depth (pressure has a positive correlation with the interaction energy), and the distance between CO2 and the saline aquifer shortens. In the high pressure range, the CO2 interaction energy gradually decreases with depth (pressure has a positive correlation with the interaction energy), and the distance between CO2 and the saline aquifer increases. Based on these conclusions, and considering stability, safety, and economic factors (currently, the suitable depth for CO2 geological sequestration in the deep saline aquifers of the Junggar Basin is considered to be 1000–3500 m), the optimal CO2 sequestration conditions are believed to be at a depth of 3500 m.
Claims
1. A method for controlling CO2 sequestration in saline aquifers based on molecular simulation and a method for constructing density-depth maps, comprising the following steps: S1. Collect geothermal gradient and pressure gradient data of typical wells in the central part of the Junggar Basin; S2. Based on temperature and pressure data, construct a coupled model of depth with temperature and pressure; S3. Construct a multi-component formation water molecule model and perform geometric optimization; S4. Perform molecular dynamics simulations under selected temperature and pressure conditions and extract equilibrium density data; S5. Based on the simulation results, fit the empirical formula for saline aquifer formation water density-depth. S6. Calculate CO2 density based on the Span-Wagner equation of state, construct a CO2 molecular model and perform geometric optimization; S7. Establish the stratified structure of CO2 and saline formation water, and conduct pressure-controlled molecular dynamics simulations.
2. The method according to claim 1, characterized in that: In step S1, the geothermal gradient is 11.6–26.5℃ / km; the pressure gradient is 10.496–11.431 MPa / km when the depth D < 4000m, and it is an abnormally high pressure section when the depth D > 4000m.
3. The method according to claim 1 or 2, characterized in that: In step S2, the coupling models of depth with temperature and pressure are shown in equations (1) and (2), respectively: T = 0.0202D + 288.15 (1) In the formula, T is the formation temperature (K); P is the formation pressure (MPa); and D is the formation depth (m).
4. The method according to claim 1 or 2, characterized in that: In step S3, a multi-component formation water molecular model is configured according to the proportions of 12,700 H2O molecules, 90 NaCl molecules, 5 KCl molecules, 3 NaHCO3 molecules, 1 CaCl2 molecule, and 1 MgCl2 molecule. An initial saline formation water model is generated using the Amorphous Cell module of Material Studio software. After geometric optimization, a stable saline formation water model is obtained.
5. The method according to claim 4, characterized in that: In step S3, the geometric optimization conditions for the saline formation water model are: COMPASS III force field, cutoff radius... Energy convergence threshold 2×10 -5 ~2×10 -3 kcal / mol, pressure convergence threshold 1×10 -3 ~2.5GPa.
6. The method according to claim 1 or 2, characterized in that: In step S4, the conditions for the molecular dynamics simulation include: The NPT ensemble was used; the simulation duration was 400 ps; the temperature control method was the Nose method, and the pressure control method was the Berendsen method; the time step was 1 fs; and the cutoff radius was 9.5–18.5 A. ° Electrostatic interactions were handled using the Ewald summation method with an accuracy of 1×10⁻⁶. -5 ~1×10 - 2 kcal / mol.
7. The method according to claim 1 or 2, characterized in that: In step S4, 10 temperature and pressure points are selected within a depth range of 200-5000m to perform molecular dynamics simulations and obtain equilibrium density data of saline formation water.
8. The method according to claim 1 or 2, characterized in that: In step S5, the linear relationship between the formation water density ρ of the saline aquifer and the depth D is shown in equation (3): In the formula, ρ is the density, in g / cm³. 3 D represents the depth of the formation, in meters (m).
9. The method according to claim 1 or 2, characterized in that: In step S7, the pressure control simulation of the layered structure includes the following steps: The optimized saline formation water model was cut along the (001) crystal plane and a unit cell was constructed. The unit cell parameters of the optimized CO2 molecular model were made consistent with the unit cell parameters of the saline formation to establish a layered structure. Molecular dynamics simulation was performed using the NVT ensemble with a total duration of 100 ps, a time step of 1.0 fs, and a total number of steps of 1 × 10⁻⁶. 5 Each step outputs one frame of trajectory file every 5000 steps.
10. The method according to claim 1 or 2, characterized in that: The method also includes steps for investigating the adsorption mechanism of CO2 in saline aquifers based on adsorption capacity, heat of adsorption, and interaction energy: The adsorption capacity, heat of adsorption, and interaction energy of CO2 in formation water were determined, and the resulting graphs were analyzed.