Molecular simulation method based on carbon dioxide flooding mixing reducer

Abstract

The invention discloses a molecular simulation method based on a carbon dioxide flooding mixing reducer. The molecular simulation method comprises the following steps: step 1, screening functional groups; 2, constructing a molecular structure model; 3, performing structure optimization on the molecular structure model to obtain a stable initial conformation; 4, analyzing parameters of the initial conformation; 5, constructing a full-atom structure model and a coarse-grained structure model by taking the molecular structure model as an object; 6, carrying out structure optimization and dynamic balance on the all-atom structure model, and calculating a molecular solubility parameter delta; 7, calculating a solubility parameter delta, and constructing a dissipative particle dynamics force field parameter; 8, constructing a dissipative particle dynamics calculation model of the mixed-phase system by taking the coarse-grained structure model and the dissipative particle dynamics force field parameters as objects; and ninthly, dynamic balance calculation parameters are set for the dissipative particle dynamic calculation model. The problems that in existing evaluation, the risk is high, and consumed time is long are solved.

Description

Molecular simulation method based on carbon dioxide displacement mixture Technical Field

[0001] This invention belongs to the field of carbon dioxide flooding and applied chemistry technology in oil and gas fields, specifically involving a molecular simulation method based on carbon dioxide flooding and demixing agents. Background Technology

[0002] Low-permeability reservoirs are widely distributed in my country, accounting for more than half of the country's total oil reserves, and possess enormous development potential. The ultra-low permeability reservoirs in the Changqing Oilfield, with their wide distribution and challenging development, are currently a crucial area for oilfield capacity building. How to rationally develop low-permeability reservoirs and effectively increase their production is of great significance to the sustainable and stable development of my country's petroleum industry. Low-permeability reservoirs have small pore throats and high sensitivity, significantly different from conventional reservoirs. Field test results show that water injection development in low-permeability reservoirs is ineffective, exhibiting problems such as slow response, low oil production capacity, and reservoir damage, while CO2 injection can achieve better results. CO2 flooding not only effectively improves crude oil recovery but also enables geological storage of CO2, reducing CO2 emissions and providing strong support for achieving the "dual carbon" goal. When the pressure reaches the minimum miscibility pressure, CO2 will also miscible with crude oil, eliminating interfacial tension and making crude oil easier to extract, significantly improving crude oil recovery. However, most oil reservoirs in China have high levels of heavy components (colloids and asphaltenes) in their crude oil, resulting in excessively high miscibility pressures that often exceed formation fracturing pressures, making CO2 miscibility flooding impossible. Therefore, the development of CO2 flooding miscibility-reducing agents has become a top priority.

[0003] Currently, the main research direction for mismixing agents is to reduce minimum miscibility pressure by designing and synthesizing surfactants. Domestic and international research on carbon dioxide mismixing agents primarily utilizes laboratory-scale experimental synthesis methods to develop and evaluate the performance of surfactant-based mismixing agents, thereby screening for those with excellent dissolution and minimum miscibility pressure reduction effects. However, this method is not only inefficient and labor-intensive, but also involves the extensive use of chemicals, causing environmental pollution and resource waste. Performance characterization requires high-pressure equipment, posing a significant risk, and is expensive and time-consuming. The structure of surfactants has a profound impact on their performance. Traditional experimental methods can only observe differences in experimental phenomena at a macroscopic level and speculate on the underlying principles, failing to accurately analyze the reasons for performance differences caused by different structures from a microscopic perspective. This makes it impossible to obtain the accurate structure-activity relationship between the structure and performance of mismixing agents, thus hindering precise guidance for subsequent mismixing agent development. Computer simulation, after years of development, has become an important tool for studying the microscopic properties of substances. By designing a series of surfactant molecular structures and employing multi-scale, multi-level computational methods, the influence of surfactant molecular structure on its performance can be obtained, efficiently selecting surfactant structures that meet the requirements. Currently, computer simulations mainly focus on the diffusion, transport, and adsorption behavior of supercritical CO2. No researchers have yet incorporated multi-scale simulations into studies of CO2 displacement mixers, leaving this field largely unexplored. Furthermore, limitations imposed by software type, user experience, and skill level mean that ensuring the consistency between computer simulation conclusions and the actual effects of surfactants remains a major challenge.

[0004] The miscibility reducer contains lipophilic hydrocarbon groups and CO2-loving ester groups. It can not only combine with polar molecules in the crude oil system and break up the aggregates of asphalt molecules, but also adsorb onto the interface between crude oil and CO2 under the action of amphiphilic properties, reducing the interfacial tension between crude oil and CO2, thereby reducing the minimum miscibility pressure.

[0005] Therefore, providing a complete, simple, and highly accurate method for the research and performance evaluation of carbon dioxide displacement mixtures based on computer simulation is a technical problem that the industry urgently needs to solve. Summary of the Invention

[0006] The purpose of this invention is to provide a molecular simulation method based on carbon dioxide displacement mixture, which solves the problems of low efficiency and high risk in macroscopic experiments in the prior art.

[0007] The technical solution adopted in this invention is a molecular simulation method based on carbon dioxide displacement and demixing agents, comprising the following steps: Step 1: Screening functional groups to obtain functional monomers with good CO2 affinity; Step 2: Constructing a molecular structure model; Step 3: Optimizing the molecular structure model to obtain a stable initial conformation; Step 4: Analyzing the initial conformation parameters and selecting compounds with good demixing agent function; Step 5: Constructing a full-atom structure model and a coarse-grained structure model based on the molecular structure model; Step 6: Optimizing the structure and kinetic equilibrium of the full-atom structure model, and calculating the molecular solubility parameter δ; Step 7: Calculating the solubility parameter δ and constructing dissipative particle dynamics force field parameters; Step 8: Constructing a dissipative particle dynamics calculation model for the miscible system based on the coarse-grained structure model and the dissipative particle dynamics force field parameters; Step 9: Setting kinetic equilibrium calculation parameters for the dissipative particle dynamics calculation model and performing dissipative particle dynamics simulation.

[0008] The present invention is further characterized in that the functional group is a functional group with de-mixing potential.

[0009] The molecular structure models include two types: the CO2 molecular structure model and the molecular structure model combining functional monomers and simulated oil.

[0010] The simulated oil is one or more of pentadecane, dodecane, and n-heptane.

[0011] The parameters for the initial conformation specifically include the calculated relevant parameters such as electron density, frontier orbital gap, abbreviated Fukui function, and adsorption reaction energy.

[0012] The full atomic structure model specifically involves constructing a CO2 atomic structure model, a simulated oil atomic structure model, and a demixing agent atomic structure model.

[0013] Specifically, the coarse-grained structure model is constructed by building a coarse-grained structure model for CO2, a coarse-grained structure model for simulated oil, and a coarse-grained structure model for the mixing agent.

[0014] The calculation of the solubility parameter δ specifically involves obtaining the repulsive force parameter α between the coarse-grained beads corresponding to the demixing agent and CO2 molecules. ij .

[0015] The molecular solubility parameter δ specifically includes the solubility parameter δ1 of the demixing agent molecule, the solubility parameter δ2 of the CO2 molecule, and the solubility parameter δ3 of the simulated oil molecule.

[0016] The dissipative particle dynamics calculation model for the miscible system is specifically a mixed dissipative particle dynamics calculation model of CO2, simulated oil and demixing agent.

[0017] The beneficial effects of this invention are as follows: This invention provides a molecular simulation method based on carbon dioxide-driven demixing agents. By constructing a model of the demixing agent molecule in MaterialsStudio software, and then performing multi-scale simulations of the demixing agent according to this system, the comprehensive analysis of the simulation data, combined with quantum chemical calculations, molecular dynamics, and dissipative particle dynamics, solves interface problems at the time and spatial scales from the microscopic to the mesoscopic that cannot be solved experimentally. This enables efficient research and development and performance evaluation of demixing agents. This invention can explore the interaction between demixing agents and crude oil and CO2 at the atomic scale, and study the interfacial behavior of different demixing agents at the mesoscopic scale, thereby enabling efficient research and development and performance evaluation of demixing agents and solving the problems of high risk, long time consumption, and high cost in experimental evaluation. Attached Figure Description

[0018] Figure 1 shows the initial molecular dynamics model of the miscible system constructed using the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention; Figure 2 shows the optimized model of the miscible system after the initial molecular dynamics model constructed using the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention; Figure 3 shows the dissipative particle dynamics model of the demixed system constructed using the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention; Figure 4 shows the molecular ball-and-stick model of the DA1 demixed system constructed using the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention; Figure 5 shows the molecular ball-and-stick model of the DA2 demixed system constructed using the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention; Figure 6 shows the molecular ball-and-stick model of the DA3 demixed system constructed using the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention; Figure 7 shows the density distribution of different demixing agents in the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention; Figure 8 shows the interfacial tension of different demixed systems constructed using the molecular simulation method based on carbon dioxide displacement and demixing agents according to the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0020] Example 1: A molecular simulation method based on carbon dioxide flooding and demixing agents, specifically including the following steps: Step 1: Screening functional groups with demixing potential to obtain functional monomers with good CO2 affinity; Step 2: Constructing molecular structure models, including two types: a CO2 molecular structure model and a molecular structure model combining functional monomers and simulated oil. The simulated oil is one or more of pentadecane, dodecane, and n-heptane; Step 3: Optimizing the molecular structure model to obtain a stable initial conformation. The parameters of the initial conformation specifically include calculated parameters such as electron density, frontier orbital gap, reduced Fukui function, and adsorption reaction energy; the coarse-grained structure model specifically involves constructing a CO2 coarse-grained structure model and a simulated oil model. Step 4: Analyze the initial conformational parameters and select compounds with good anti-mixing properties; Step 5: Using the molecular structure model as the object, construct the full-atom structure model and the coarse-grained structure model; the full-atom structure model specifically involves constructing the CO2 atomic structure model, the simulated oil atomic structure model, and the anti-mixing agent atomic structure model respectively; Step 6: Optimize the structure and kinetic equilibrium of the full-atom structure model, and calculate the molecular solubility parameter δ, specifically the anti-mixing agent molecule solubility parameter δ1, the CO2 molecule solubility parameter δ2, and the simulated oil molecule solubility parameter δ3; Step 7: Calculate the solubility parameter δ to obtain the repulsive force parameter a between the corresponding coarse-grained beads between the anti-mixing agent and CO2 molecules. ij Step 8: Using the coarse-grained structure model and the dissipative particle dynamics force field parameters as the objects, construct a dissipative particle dynamics calculation model for the miscible system. Specifically, the dissipative particle dynamics calculation model for the miscible system is a mixed dissipative particle dynamics calculation model of CO2, simulated oil and demixing agent. Step 9: Set the dynamic equilibrium calculation parameters for the dissipative particle dynamics calculation model and perform dissipative particle dynamics simulation.

[0021] As shown in Figures 1-3, the present invention utilizes a molecular simulation method based on carbon dioxide displacement and demixing agents. This method combines quantum chemical simulation with molecular dynamics simulation and mesoscopic dynamics. In Figure 1, the dissipative particle dynamics model of the demixing system consists of two SiO2 layers. The left side of the middle layer between the two SiO2 layers represents the molecular structure model of CO2, while the right side represents the molecular structure model of the combination of simulated oil and demixing agent. Figure 2 shows the optimized model of the initial molecular dynamics model of the constructed miscible system. It can be seen that in the optimized structure, some CO2 has already mixed with the simulated oil, thus realizing the simulation of CO2 displacement of oil. Figure 3 shows the dissipative particle dynamics model of the demixing system. The left side of the figure represents the coarse-grained model of the simulated oil, the middle side represents the coarse-grained model of the demixing agent, and the right side represents the coarse-grained model of CO2. This model verifies the behavior mechanism of the demixing agent at the interface.

[0022] Example 2, based on Example 1, uses the Geometry Optimization tool in the Forcite module to perform geometric optimization on the miscible system model, as shown in Figure 4. Subsequently, using the Annea tool, five annealing cycle kinetic calculations were performed under the canonical ensemble (NVT) at 300~500K (T=273.15+t(℃)) to obtain an energy- and structurally stable model.

[0023] All force fields used were selected from COMPASS II, charges were selected from Forcefield assigned, and van der Waals interactions and electrostatic interactions were selected from Atom-based and Ewald methods, respectively.

[0024] The pressure and temperature control methods used were Nose and Berendsen, respectively.

[0025] This embodiment includes a computer simulation performance characterization of an ether-based demixing system. Specifically, it includes the following steps: Step 1: Using the Sketch tool in Materials Studio software, draw the molecular structure model of CO2, the demixing agent, and the simulated oil in the Visualizer interface. The demixing agent includes one or more of ethylene glycol butyl ether (BCS), toluene (MB), benzene (Be), and p-xylene (PX); the simulated oil is a straight-chain alkane with different carbon chain lengths, including one or more of pentadecane, dodecane, and n-heptane; the functional groups are screened to obtain functional monomers with good CO2 affinity.

[0026] Step 2: Save the two molecular structure models as coordinate files in .xsd format. Based on density functional theory, optimize the structure of the two molecular structure models to obtain a stable initial conformation.

[0027] Step 3: Calculate the atomic charge of the confining electrostatic potential using the B3LYP / 6-31+g(d) method, and obtain the van der Waals parameters and bonding parameters of each atom in the above molecule; for the demixing agent molecule and CO2 molecule calculated by the B3LYP / 6-31+g(d) method, analyze their electron density, frontier orbital gap, abbreviated Fukui function, and adsorption reaction energy, etc.

[0028] Step 4: Analyze the parameters of the initial conformation, specifically by comparing the affinity of each demixing agent for CO2 and selecting compounds with good demixing agent function.

[0029] Step 5: Using the molecular structure model as the object, construct a full-atom structure model using the Amorphous Cell module. DA1 (BCS+Be+MB+PX) is used as the de-mixing agent component, with a molecular count of 50. 2000 CO2 molecules and 500 simulated oil molecules are added, and the density is set to 1.0 g / cm³. 3 Full atomic models of CO2, simulated oil, and mixing agent were constructed respectively. Using the optimized molecular model as the object, MaterialsStudio software was used for modeling, and coarse-grained structural models were constructed by using Build→Build Meso structure to obtain coarse-grained structural models of CO2, simulated oil, and mixing agent.

[0030] Step Six: Optimize the obtained all-atom structure model. In the .xsd file, select the structure corresponding to the lowest energy value for the next calculation. Perform kinetic equilibrium calculations on the .xsd file by selecting Forcite→Calculation→Dynamics→More, using NVT as the ensemble, setting the temperature to 365K, selecting appropriate parameters in sequence, and checking "Optimize cell" to run. For the obtained .xtd trajectory file, select Forcite→Calculation→CohesiveEnergy Density to obtain the solubility parameter δ. Specifically, the molecular solubility parameter δ includes the solubility parameter δ1 for the demixing agent molecule, the solubility parameter δ2 for the CO2 molecule, and the solubility parameter δ3 for the simulated oil molecule.

[0031] Step 7: Calculate the corresponding repulsive force parameter a between the coarse-grained beads based on the obtained solubility parameters δ1 (of the demixing agent molecule), δ2 (of the CO2 molecule), and δ3 (of the simulated oil molecule). ij Based on the obtained values, select Modules→Mesocite→Force field Manager→DPD→Type, select Reduced as the unit, input the values ​​of 70 for the repulsive force between similar beads and 80 for the repulsive force between different types of beads in the coarse-grained structure model obtained in step 5 into the force field parameters, and then click Create to construct the dissipative particle dynamic force field parameters.

[0032] Step 8: Using the coarse-grained structure model and dissipative particle dynamics force field parameters as the object, select Build→Build Meso structure→Meso structure Template, set all range (XYZ) values ​​to 100.0, select Build→Build Meso structure→Meso structure, combine the experimental results, input the system components and their proportions according to the corresponding ratio, set the density to 3, and click Build to construct the dissipative particle dynamics calculation model of the miscible system, as shown in Figure 4.

[0033] Step 9: Perform a dissipative particle dynamics calculation model simulation. Select Modules→Mesocite→DPD→More, set the temperature to 365K, and select appropriate parameters in sequence to obtain the equilibrium dissipative particle dynamics calculation model. Save the .xtd trajectory file in the result file. The dissipative particle dynamics calculation model calculation is complete. Analyze the result file.

[0034] Example 3, based on Example 1, uses the Geometry Optimization tool in the Forcite module to perform geometric optimization on the miscible system model, as shown in Figure 5. Subsequently, using the Annea tool, five annealing cycle kinetic calculations were performed under the canonical ensemble (NVT) at 300–500 K (T = 273.15 + t (°C)) to obtain an energy- and structurally stable model.

[0035] All force fields used were selected from COMPASS II, charges were selected from Forcefield assigned, and van der Waals interactions and electrostatic interactions were selected from Atombased and Ewald methods, respectively.

[0036] The pressure and temperature control methods used were Nose and Berendsen, respectively.

[0037] This embodiment includes a computer simulation performance characterization of an ether-based demixing system. Specifically, it includes the following steps: Step 1: Using the Sketch tool in Materials Studio software, draw the molecular structure model of CO2, the demixing agent, and the simulated oil in the Visualizer interface. The demixing agent includes one or more of 2,2,4-trimethyl-1,3-pentanediol diisobutyrate (TXIB) and ethanol (EA); the simulated oil is a straight-chain alkane with different carbon chain lengths, including one or more of pentadecane, dodecane, and n-heptane; the functional groups are screened to obtain functional monomers with good CO2 affinity.

[0038] Step 2: Save the two molecular structure models as coordinate files in .xsd format. Based on density functional theory, optimize the structure of the two molecular structure models to obtain a stable initial conformation.

[0039] Step 3: Calculate the atomic charge of the confined electrostatic potential using the B3LYP / 6-31+g(d) method, and obtain the van der Waals parameters and bonding parameters for each atom; analyze the electron density, frontier orbital gap, abbreviated Fukui function, and adsorption reaction energy of the demixing agent molecule and CO2 molecule calculated by the B3LYP / 6-31+g(d) method.

[0040] Step 4: Analyze the parameters of the initial conformation, specifically by comparing the affinity of each demixing agent for CO2 and selecting compounds with good demixing agent function.

[0041] Step 5: Using the molecular structure model as the object, construct a full-atom structure model using the Amorphous Cell module. DA2 (TXIB+EA) is used as the demixing agent component, with a molecular count of 50. 2000 CO2 molecules and 500 alkane molecules are added, and the density is set to 1.0 g / cm³. 3 Full-atomic models of CO2 atomic structure, simulated oil atomic structure, and demixing agent atomic structure were constructed respectively. Using the structure-optimized molecular model as the object, Materials Studio software was used for modeling, and coarse-grained structural models were constructed by using Build→Build Meso structure to obtain coarse-grained structural models of CO2, simulated oil, and demixing agent.

[0042] Step Six: Optimize the obtained all-atom structure model. In the .xsd file, select the structure corresponding to the lowest energy value for the next calculation. Perform kinetic equilibrium calculations on the .xsd file by selecting Forcite→Calculation→Dynamics→More, using NVT as the ensemble, setting the temperature to 365K, selecting appropriate parameters in sequence, and checking "Optimize cell" to run. For the obtained .xtd trajectory file, select Forcite→Calculation→CohesiveEnergy Density to obtain the solubility parameter δ. Specifically, the molecular solubility parameter δ includes the solubility parameter δ1 for the demixing agent molecule, the solubility parameter δ2 for the CO2 molecule, and the solubility parameter δ3 for the simulated oil molecule.

[0043] Step 7: Calculate the corresponding repulsive force parameter aij between coarse-grained beads for the obtained solubility parameters δ1 (de-mixing agent molecule), δ2 (CO2 molecule), and δ3 (simulated oil molecule). Based on the obtained values, select Modules→Mesocite→Force field Manager→DPD→Type, using Reduced as the unit. Input the values ​​of 70 for the repulsive force between beads of the same type and 80 for the repulsive force between beads of different types in the coarse-grained structure model obtained in Step 5 into the force field parameters, and then click Create to construct the dissipative particle dynamics force field parameters.

[0044] Step 8: Using the coarse-grained structure model and dissipative particle dynamics force field parameters as the object, select Build→Build Meso structure→Meso structure Template, set all range (XYZ) values ​​to 100.0, select Build→Build Meso structure→Meso structure, combine the experimental results, input the system components and their proportions according to the corresponding ratio, set the density to 3, and click Build to construct the dissipative particle dynamics calculation model of the miscible system, as shown in Figure 5.

[0045] Step 9: Perform a dissipative particle dynamics calculation model simulation. Select Modules→Mesocite→DPD→More, set the temperature to 365K, and select appropriate parameters in sequence to obtain the equilibrium dissipative particle dynamics calculation model. Save the .xtd trajectory file in the result file. The dissipative particle dynamics calculation model calculation is complete. Analyze the result file.

[0046] Example 4, based on Example 1, uses the Geometry Optimization tool in the Forcite module to perform geometric optimization on the miscible system model, as shown in Figure 6. Subsequently, using the Annea tool, five annealing cycle kinetic calculations were performed under the canonical ensemble (NVT) at 300–500 K (T = 273.15 + t (°C)) to obtain an energy- and structurally stable model.

[0047] All force fields used were selected from COMPASS II, charges were selected from Forcefield assigned, and van der Waals interactions and electrostatic interactions were selected from Atom-based and Ewald methods, respectively.

[0048] The pressure and temperature control methods used were Nose and Berendsen, respectively.

[0049] This embodiment describes the computer simulation performance characterization of an ester-based demixing system. Specifically, it includes the following steps: Step 1: Using the Sketch tool in Materials Studio software, draw the molecular structure model of CO2, the demixing agent, and the simulated oil in the Visualizer interface. The demixing agent includes one or more of lauryl polyether (PPG) and ethanol (EA); the simulated oil is a straight-chain alkane with different carbon chain lengths, including one or more of pentadecane, dodecane, and n-heptane; the functional groups are screened to obtain functional monomers with good CO2 affinity.

[0050] Step 2: Save the two molecular structure models as coordinate files in .xsd format. Based on density functional theory, optimize the structure of the two molecular structure models to obtain a stable initial conformation.

[0051] Step 3: Calculate the atomic charge of the confined electrostatic potential using the B3LYP / 6-31+g(d) method, and obtain the van der Waals parameters and bonding parameters for each atom; analyze the electron density, frontier orbital gap, abbreviated Fukui function, and adsorption reaction energy of the demixing agent molecule and CO2 molecule calculated by the B3LYP / 6-31+g(d) method.

[0052] Step 4: Analyze the parameters of the initial conformation, specifically by comparing the affinity of each demixing agent for CO2 and selecting compounds with good demixing agent function.

[0053] Step 5: Using the molecular structure model as the object, construct a full-atom structure model using the Amorphous Cell module. DA3 (PPG+EA) is used as the demixing agent component, with a molecular count of 50. 2000 CO2 molecules and 500 alkane molecules are added, and the density is set to 1.0 g / cm³. 3 Full atomic models of CO2, simulated oil, and demixing agent were constructed respectively. Using the optimized molecular model as the object, Materials Studio software was used for modeling, and coarse-grained structural models were constructed using Build→BuildMesostructure to obtain coarse-grained structural models of CO2, simulated oil, and demixing agent.

[0054] Step Six: Optimize the obtained all-atom structure model. In the .xsd file, select the structure corresponding to the lowest energy value for the next calculation. Perform kinetic equilibrium calculations on the .xsd file by selecting Forcite→Calculation→Dynamics→More, using NVT as the ensemble, setting the temperature to 365K, selecting appropriate parameters in sequence, and checking "Optimize cell" to run. For the obtained .xtd trajectory file, select Forcite→Calculation→CohesiveEnergy Density to obtain the solubility parameter δ. Specifically, the molecular solubility parameter δ includes the solubility parameter δ1 for the demixing agent molecule, the solubility parameter δ2 for the CO2 molecule, and the solubility parameter δ3 for the simulated oil molecule.

[0055] Step 7: Calculate the corresponding repulsive force parameter aij between coarse-grained beads for the obtained solubility parameters δ1 (de-mixing agent molecule), δ2 (CO2 molecule), and δ3 (simulated oil molecule). Based on the obtained values, select Modules→Mesocite→Force field Manager→DPD→Type, using Reduced as the unit. Input the values ​​of 70 for the repulsive force between beads of the same type and 80 for the repulsive force between beads of different types in the coarse-grained structure model obtained in Step 5 into the force field parameters, and then click Create to construct the dissipative particle dynamics force field parameters.

[0056] Step 8: Using the coarse-grained structure model and dissipative particle dynamics force field parameters as the object, select Build→Build Meso structure→Meso structure Template, set all range (XYZ) values ​​to 100.0, select Build→Build Meso structure→Meso structure, combine the experimental results, input the system components and their proportions according to the corresponding ratio, set the density to 3, and click Build to construct the dissipative particle dynamics calculation model of the miscible system, as shown in Figure 6.

[0057] Step 9: Perform a dissipative particle dynamics calculation model simulation. Select Modules→Mesocite→DPD→More, set the temperature to 365K, and select appropriate parameters in sequence to obtain the equilibrium dissipative particle dynamics calculation model. Save the .xtd trajectory file in the result file. The dissipative particle dynamics calculation model calculation is complete. Analyze the result file.

[0058] Verification Example 1 Test conditions: The oil sample used was simulated oil from Changqing Oilfield, and the German KRUSS GMGH fully automatic interfacial tensiometer was used.

[0059] The interlayer pressure of 19.74 MPa and the temperature of 91.73℃ in Huang 39 block of Changqing Oilfield were used as a reference.

[0060] Test method: Step 1: Clean all experimental instruments and pipelines with petroleum ether, and then purge with pressurized N2 to remove residual petroleum ether.

[0061] Step 2: Use CO2 to purge again, then start heating the system.

[0062] Step 3: Use a booster pump to raise the CO2 gas in the droplet chamber to the set pressure value, and at the same time raise the oil sample prepared in the PVT instrument to the set pressure value, and heat it to stabilize the temperature of the droplet chamber at 91℃.

[0063] Step 4: Slowly press a drop of crude oil into the probe of the droplet chamber, select an oil droplet that can stably hang and has the largest possible shape, and let it reach equilibrium with CO2. The camera system captures and records the image of the oil droplet, and the software system automatically calculates the interfacial tension value between CO2 and crude oil.

[0064] Step 5: Adjust the experimental pressure and repeat steps 3 and 4 above to measure the interfacial tension of the CO2-crude oil system under different pressure conditions.

[0065] Step 6: Fit the series of interfacial tension data and use the extrapolation method to calculate the mixed-phase pressure when the interfacial tension is 0.

[0066] The application effects of each embodiment are as follows: using the model in Embodiment 1 to perform simulation data analysis, and referring to crude oil from a certain block of Changqing Oilfield, the minimum miscibility pressure is calculated by performing interfacial tension tests after adding a demixing agent.

[0067] The densities of different mismixing agents are shown in Figure 7. DA2 has the highest density at the interface, indicating that it has the strongest tendency to adsorb at this interface. This suggests that DA2 has a strong potential to reduce the minimum miscibility pressure. DA3 is the next most effective, while DA1 is the least effective. The interfacial tensions of different mismixing systems are shown in Figure 8. The interfacial tension between simulated oil and carbon dioxide in Changqing Oilfield is not highly sensitive to pressure, resulting in a high initial miscibility pressure. The addition of mismixing agents significantly reduced the interfacial tension between the simulated oil and carbon dioxide, thereby substantially reducing the minimum miscibility pressure. In particular, the addition of DA2 resulted in a 30.4% reduction in interfacial tension, followed by DA3. DA1 had the least mismixing effect, which is highly consistent with the simulation results. Therefore, the mismixing agents screened by the carbon dioxide flooding mismixing agent development system described in Example 1 have a good effect on reducing interfacial tension and are beneficial for reducing the minimum miscibility pressure. This demonstrates that the computer simulation-based carbon dioxide flooding mismixing agent development method and system of this invention have good practicality.

Claims

1. A molecular simulation method based on carbon dioxide displacement mixtures, characterized in that, Includes the following steps: Step 1: Screen functional groups to obtain functional monomers with good CO2 affinity; Step 2: Construct a molecular structure model; Step 3: Optimize the molecular structure model to obtain a stable initial conformation; Step 4: Analyze the parameters of the initial conformation and select compounds with good demixing properties; Step 5: Using the molecular structure model as the object, construct a full-atom structure model and a coarse-grained structure model; Step 6: Optimize the structure and kinetic equilibrium of the full-atom structure model, and calculate the molecular solubility parameter δ; Step 7: Calculate the solubility parameter δ and construct dissipative particle dynamics force field parameters; Step 8: Using the coarse-grained structure model and dissipative particle dynamics force field parameters as the object, construct a dissipative particle dynamics calculation model for the miscible system; Step 9: Set the kinetic equilibrium calculation parameters for the dissipative particle dynamics calculation model and perform dissipative particle dynamics simulation.

2. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, The functional group is a functional group with demixing potential.

3. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, The molecular structure models include two types: the CO2 molecular structure model and the molecular structure model combining functional monomers and simulated oil.

4. The molecular simulation method based on carbon dioxide displacement mixture according to claim 3, characterized in that, The simulated oil is one or more of pentadecane, dodecane, and n-heptane.

5. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, The parameters for the initial conformation specifically include the calculated relevant parameters such as electron density, frontier orbital gap, abbreviated Fukui function, and adsorption reaction energy.

6. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, The full atomic structure model specifically involves constructing a CO2 atomic structure model, a simulated oil atomic structure model, and a demixing agent atomic structure model.

7. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, Specifically, the coarse-grained structure model is constructed by building a coarse-grained structure model for CO2, a coarse-grained structure model for simulated oil, and a coarse-grained structure model for a dispersant.

8. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, The calculation of the solubility parameter δ specifically involves obtaining the repulsive force parameter a between the coarse-grained beads corresponding to the demixing agent and CO2 molecules. ij .

9. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, The molecular solubility parameter δ specifically refers to the solubility parameter δ1 of the demixing agent molecule, the solubility parameter δ2 of the CO2 molecule, and the solubility parameter δ3 of the simulated oil molecule.

10. The molecular simulation method based on carbon dioxide displacement mixture according to claim 1, characterized in that, The dissipative particle dynamics calculation model of the miscible system is specifically a mixed dissipative particle dynamics calculation model of CO2, simulated oil and demixing agent.

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