Shale oil demulsifier screening and compounding method based on molecular dynamics simulation

Abstract

The invention relates to the field of crude oil produced liquid ground gathering and transportation treatment and oilfield chemical calculation, and particularly discloses a shale oil demulsifier screening and compounding method based on molecular dynamics simulation. The method comprises the following steps: firstly, constructing a standardized oil-water interface molecular model containing asphaltene, colloid and a demulsifier to be evaluated on the basis of shale oil produced liquid components; then, molecular dynamics simulation is carried out under 313 K and 353 K respectively, and a system balance trajectory is obtained; extracting and calculating two key microcosmic indexes from the trajectory, wherein the two key microcosmic indexes comprise the difference E of unit area interface binding energy of a demulsifier and asphaltene / colloid and the thermally induced diffusion enhancement rate gamma calculated based on asphaltene and colloid diffusion coefficients; and finally, associating the microscopic index with the dehydration rate measured by a macroscopic bottle test experiment, establishing a demulsifier performance prediction criterion taking a high E (absolute value) and a high gamma value as a core, and performing classification and rational compounding guidance on the demulsifier according to the criterion. According to the method, rapid and low-cost virtual pre-screening and sorting of the demulsifier performance are realized by using microscopic simulation indexes. The embodiment shows that the demulsifier AR (robust adsorption type) and the demulsifier PR (strong disturbance type) can be compounded according to the mass ratio of 1: 1 based on the criterion, the dehydration rate of the demulsifier at the temperature of 70 DEG C and the concentration of 400 ppm reaches 77.52% and is remarkably superior to that of a single component and other compounding, the accuracy of the prediction criterion and the application value in guiding dominant compounding are verified, and the prediction criterion has a wide application prospect. And a new strategy is provided for rational design and efficient screening of the shale oil demulsifier.

Description

Technical Field

[0001] This invention relates to the fields of crude oil produced fluid surface gathering and treatment and oilfield chemical calculations, specifically to a method for screening and compounding shale oil demulsifiers based on molecular dynamics simulations. Background Technology

[0002] Petroleum, as a vital strategic resource, plays a crucial role in economic development and national security. Due to the large-scale development of unconventional oil and gas resources such as shale oil, the efficient dehydration of produced fluids has become a key link in ensuring the normal gathering, transportation, and production of oilfields. However, shale oil produced fluids, rich in asphaltenes, gums, and other natural emulsifiers, form extremely stable oil-water emulsions, posing a significant challenge to crude oil dehydration. In contrast, adding chemical demulsifiers to the dehydration process can achieve oil-water separation by rapidly disrupting the interfacial film, making it the most economical and effective treatment method currently available. Extensive research has confirmed the feasibility and broad application prospects of chemical demulsifiers in shale oil dehydration. However, demulsifier molecules are complex and diverse, mainly including polyethers with different block structures, molecular weight distributions, and functional group types. Therefore, in actual dehydration processes, how to efficiently and accurately screen and rationally formulate demulsifiers has become one of the core challenges in demulsifier application and research.

[0003] Molecular dynamics simulations (MD) can reveal microscopic dynamic information such as the interaction and competitive adsorption between interfacial active substances (e.g., asphaltenes and resins) and demulsifier molecules at the oil-water interface at the atomic scale, and are therefore widely used in the study of emulsion stability and demulsification mechanisms. For example, existing studies have used MD methods to demonstrate that multi-branched demulsifiers have better interfacial displacement capabilities than linear structures (J. Polym. Res., 2025, 32(5): 185). However, existing MD studies mostly focus on microscopic mechanism explanations and qualitative descriptions, and the extracted analytical parameters (e.g., molecular configuration, concentration distribution) are mostly static or equilibrium indicators, failing to deeply explore microscopic descriptors that can dynamically, sensitively, and quantitatively characterize the core functions of demulsifiers. Specifically, existing methods are difficult to quantify the destructive effect of demulsifiers on the interfacial asphaltenes network structure, which is the key to determining their macroscopic demulsification efficiency. In contrast, macroscopic bottle tests can directly measure the dehydration rate of demulsifiers, which is the ultimate standard for evaluating their performance. However, traditional trial-and-error experimental screening is time-consuming, costly, and prone to randomness. It is also difficult to reverse-engineer universal molecular design rules from massive experimental data. There is a lack of effective and quantifiable link between the two techniques.

[0004] To address the aforementioned issues, this invention aims to deeply integrate molecular dynamics simulation methods with macroscopic experimental verification to construct a shale oil demulsifier screening and formulation method based on microscopic indicators. The core of this method lies in innovatively defining and extracting two key quantitative indicators directly related to demulsifier function from simulation data: the difference in interfacial binding energy per unit area between the demulsifier and asphaltene / colloids, ∆E (reflecting the demulsifier's competitive adsorption advantage), and the thermally induced diffusion enhancement rate, γ (reflecting the demulsifier's dynamic destructive ability on the interfacial network). By correlating these two microscopic indicators with the macroscopic dehydration rate, reliable performance prediction criteria are established. This method not only enables rapid, low-cost virtual pre-screening and rational ranking of the performance of demulsifiers with different structures, but also scientifically guides the advantageous formulation of demulsifiers based on indicator analysis (such as distinguishing between robust adsorption, strong adsorption, and strong perturbation types), thus completing a full closed loop from microscopic mechanism understanding to macroscopic performance prediction and optimization. This has significant theoretical guiding significance for the targeted research and application of demulsifiers. Summary of the Invention

[0005] This invention specifically relates to a method for screening and compounding demulsifiers for shale oil based on molecular dynamics simulations. By combining molecular dynamics simulations with macroscopic experiments, this method can quantify the static adsorption strength and dynamic displacement efficiency of demulsifiers at the interface at the atomic scale, and directly correlate them with macroscopic dehydration efficiency, establishing reliable performance prediction criteria. By calculating key microscopic parameters such as the difference in interfacial binding energy per unit area between the demulsifier and asphaltenes / colloids, and the thermally induced diffusion enhancement rate, the effectiveness of demulsifiers can be rationally predicted and ranked. This method is applicable to demulsifiers of various structures and their compounding systems, and has significant application value in the field of computer-aided rational design and efficient screening of demulsifiers.

[0006] The implementation process of this invention is briefly explained below. First, a standardized oil-water interface molecular model containing characteristic components of shale oil is constructed, and demulsifier molecules with different structures to be screened are placed in the system. Then, molecular dynamics simulations are performed at two different temperatures to obtain the system's equilibrium trajectory. Subsequently, two key microscopic indicators are extracted from the simulated trajectory: the difference in interfacial binding energy per unit area between the demulsifier and asphaltenes / colloids, ∆E, and the thermally induced diffusion enhancement rate of asphaltenes. Finally, the microscopic indicators are correlated with the dehydration rate measured by macroscopic bottle tests to establish microscopic analysis prediction criteria, which guide the performance optimization and rational compounding of demulsifiers.

[0007] This invention specifically relates to a method for screening and compounding shale oil demulsifiers based on molecular dynamics simulations, which is implemented through the following specific steps:

[0008] (1) Construction of a standardized oil-water interface molecular model system: Based on the thermogravimetric and group composition analysis results of the target shale oil produced fluid, the representative proportions of each component in the model were determined. Using molecular simulation software such as Materials Studio, a periodic oil-water interface box model was constructed, which includes an oil phase (simulating the composition of real alkanes and aromatics), an aqueous phase (containing ions to simulate salinity), and an interfacial active layer (using the classic C5Pe model as asphaltene and the 6-methyl-dibenzothiophene model as resin). Different demulsifier molecules with different structures to be evaluated were placed in the system, and then the energy of the overall system was minimized to achieve a stable configuration.

[0009] (2) Perform multi-temperature molecular dynamics simulations: Use software such as Materials Studio, GROMACS, or LAMMPS for simulation. Force fields suitable for hydrocarbon and interfacial systems, such as COMPASS II and OPLS-AA, are selected. First, the system is optimized and balanced under the NPT ensemble. To calculate key microscopic parameters, molecular dynamics simulations are run in production under the NVT ensemble at two temperature points (313 K and 353 K, or two other temperature points with a reasonable temperature difference). The simulation time is typically set to 2 ns ~ 40 ns, with an optimal NPT equilibration period of at least 500 ps and an NVT production simulation of 2000 ps. The entire simulation is controlled by a Nose-Hoover thermostat. Pressure coupling can be achieved using methods such as Berendsen, and the target pressure can be set from atmospheric pressure to 10 MPa. Electrostatic interactions are calculated using the Ewald or PME method, and van der Waals interactions are calculated using the Atom-based method, with a cutoff radius set to 8 Å ~ 15 Å. The integration step size is set to 0.5 fs ~ 2.0 fs. The equilibrium trajectory is saved for subsequent analysis.

[0010] (3) Extracting molecular simulation trajectories and calculating microscopic indices: Calculate the interfacial binding energy per unit area at the interface between the asphaltene / collagen layer and the aqueous phase, and at the interface between each demulsifier molecule and the aqueous phase, from the equilibrium trajectories. The interfacial binding energy is calculated using the energy component analysis method, i.e., extracting the configuration from the equilibrium trajectory and calculating the energy of the complete system, isolated interface components, and isolated solvent, respectively. The difference is the interfacial binding energy, and the ratio of the interfacial binding energy to the interface area is the interfacial binding energy per unit area. Calculate the difference in interfacial binding energy per unit area between the demulsifier and the asphaltene / collagen in the same system, i.e., ∆E = E_demulsifier - E_asphaltene / collagen. The larger the absolute value, the stronger the competitive adsorption advantage of the demulsifier molecule relative to the natural emulsifier at the interface. Using the Einstein relation, calculate the mean square displacement of the asphaltene and colloidal molecules in the simulated trajectories at 313 K and 353 K in the same demulsifier system, and then obtain their diffusion coefficient D in the interface normal direction. 313K and D353K According to the formula γ=D 353K / D 313K Calculate the thermally induced diffusion enhancement rate for each system. The γ value is used to dynamically characterize the damage of the demulsifier to the interfacial asphaltene network structure; the higher the γ value, the more significant the network loosening.

[0011] (4) Establish micro-macro correlation criteria and guide application: Determine the dehydration rate of each demulsifier on the target shale oil produced fluid through standard bottle test experiments (refer to SY / T 5281-2000 "Test Method for Performance of Crude Oil Demulsifiers (Bottle Test Method)"). Analyze the correlation between the demulsifier and the dehydration rate and the interfacial binding energy per unit area ∆E and the thermally induced diffusion enhancement rate γ obtained in step (3). Establish micro-performance prediction criteria for the interfacial binding energy per unit area ∆E and the thermally induced diffusion enhancement rate γ. Based on this criterion, demulsifiers can be classified (for example, those with large absolute values ​​of ∆E and medium γ are strong adsorption types, those with high values ​​of both ∆E and γ are robust adsorption types, and those with medium values ​​of ∆E and high γ are strong perturbation types), and guide the rational compounding of demulsifiers with complementary advantages. Optimize the compounding ratio through experimental verification.

[0012] The purpose of this invention is to construct an accurate, efficient, and mechanism-clear method for predicting and screening the performance of demulsifiers. This method can reveal the structure-activity relationship of demulsifiers at the molecular level, realize virtual pre-screening and rational compounding guidance, significantly improve the R&D efficiency of demulsifiers, and has important theoretical value and application prospects in the field of oilfield chemistry. Attached Figure Description

[0013] Figure 1 is a schematic diagram of the molecular simulation model and the molecular configuration of the demulsifier constructed in this invention.

[0014] Figure 2 is a bar chart comparing the interfacial binding energy per unit area of ​​six demulsifiers (AP, AE, AR, BP, PR, SP) at 313K and 353K in an embodiment of the present invention.

[0015] Figure 3 is a bar chart comparing the asphaltene / colloid diffusion coefficient and thermally induced diffusion enhancement rate of six demulsifiers (AP, AE, AR, BP, PR, SP) at 313K and 353K in an embodiment of the present invention.

[0016] Figure 4 is a bar chart showing the dehydration rates of six demulsifiers (AP, AE, AR, BP, PR, SP) and the dehydration rates of demulsifier combinations in embodiments of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] Example 1

[0019] (1) Based on the component analysis of the produced fluid of the target shale oil, a specific model was constructed according to the relevant data: using alkanes (24 hexane molecules, 24 cyclohexane molecules, 22 heptane molecules, 22 octane molecules, 48 ​​nonane molecules, 42 dodecane molecules, and 29 hexadecane molecules), aromatic hydrocarbons (32 benzene molecules and 54 toluene molecules); the aqueous phase contains 1500 water molecules and 15 Na molecules. + and 15 Cl - The interface layer contains 10 C5Pe asphaltene molecules and 32 resin molecules. The model box dimensions are 45 Å × 45 Å × 100 Å. The demulsifier molecules to be screened, AP, AE, AR, BP, PR, and SP, are placed between the asphaltene / resin layer and the aqueous phase, respectively, and energy minimization is performed. The convergence criterion is an energy change of less than 1.0 eE. -4 kcal / mol, with an action force less than 0.005 kcal / mol / Å.

[0020] (2) Multi-temperature molecular dynamics simulations were performed for each demulsifier system. Using the COMPASS II force field, NPT ensemble equilibrium simulations and NVT ensemble simulations were performed sequentially at 313 K and 353 K. The NPT simulation time was set to 500 ps, ​​and the NVT simulation time was set to 2000 ps, ​​with an integration step size of 1.0 fs. The Nose-Hoover method was used for temperature control, and the Berendsen method was used for pressure coupling. The target pressure was 1.013 e -4 GPa. The van der Waals and electrostatic interaction cutoff radius was set to 12.5 Å. The equilibrium trajectory was saved for analysis.

[0021] (3) Extracting molecular simulation trajectories and calculating microscopic indices: Calculate the interfacial binding energy per unit area at the interface between the asphalt / colloid layer and the aqueous phase, and at the interface between each demulsifier molecule and the aqueous phase, from the equilibrium trajectories. The unit is kcal·mol. -1 ·Å -2 Calculate the difference in interfacial binding energy per unit area between the demulsifier and asphaltene / resin in the same system, ∆E. Calculate the thermally induced diffusion enhancement rate γ: Calculate the diffusion coefficient D of asphaltene and resin molecules at 353 K and 313 K in the same demulsifier system. 313K and D 353K According to the formula γ=D 353K / D 313K The calculation yielded the result.

[0022] (4) Correlate microscopic indicators with macroscopic experiments to construct predictive criteria and guide applications. The dehydration rate of each demulsifier was determined by bottle test experiments. Correlation analysis showed that the difference in interfacial binding energy per unit area between the demulsifier and asphaltene / rubber, ∆E (absolute value) and γ value, were both positively correlated with the dehydration rate. Thus, the screening criteria were established: demulsifiers with high difference in interfacial binding energy per unit area, ∆E (absolute value), and high thermally induced diffusion enhancement rate, γ, were preferred. According to this criterion, demulsifier AR was classified as robust adsorption type, demulsifier PR was classified as strongly perturbed type, and demulsifier AE was classified as strongly adsorption type. The demulsifiers were compounded at a mass ratio of 1:1 and tested under test conditions (70℃, 400 ppm concentration). The results showed that the dehydration rate of AR / PR reached a peak of 77.52%, which was significantly better than that of single components and other compound groups.

[0023] Figure 1 This is a schematic diagram of the molecular simulation model and the molecular configuration of the demulsifier constructed in this invention. Figure 1 (a) To construct a standardized molecular model of the oil-water interface and perform molecular dynamics simulations; Figure 1 (b) to (g) are the molecular models of demulsifiers AP, AE, AR, PR, BP and SP, respectively.

[0024] Figure 2 This is a bar chart comparing the interfacial bonding energy per unit area of ​​six demulsifiers (AP, AE, AR, BP, PR, SP) at 313K and 353K in an embodiment of the present invention. The results show that demulsifiers AR and AE have relatively high interfacial bonding energy per unit area (absolute value), while the asphaltene / resin interfacial bonding energy per unit area (absolute value) is relatively low, indicating their optimal macroscopic demulsification potential. Conversely, demulsifier SP has relatively low values ​​in both indicators, indicating its worst macroscopic demulsification potential.

[0025] Figure 3 This is a bar chart comparing the asphaltene / resin diffusion coefficients and thermally induced diffusion enhancement rates of six demulsifiers (AP, AE, AR, BP, PR, SP) at 313K and 353K in an embodiment of the present invention. The results show that demulsifiers AR and PR have the highest thermally induced diffusion enhancement rates, indicating that they have good interfacial disturbance effects; according to... Figure 2 and Figure 3 The prediction results show that AE is a strong adsorption type, AR is a robust adsorption type, and PR is a strong perturbation type.

[0026] Figure 4 (a) is a bar chart showing the dehydration rates of six demulsifiers (AP, AE, AR, BP, PR, SP) and the dehydration rates of demulsifier combinations in embodiments of the present invention. The comparison shows that the predicted sequences are highly consistent with the experimental performance sequences, verifying the reliability of the microscopic prediction criteria. Figure 4(b) demonstrates that the criterion-guided compound system (AR / PR) exhibits the highest dehydration rate, proving the application value of this method in guiding rational compounding and achieving synergistic effects.

Claims

1. The present application relates particularly to a shale oil demulsifier screening and compounding method based on molecular dynamics simulation, characterized in that, Includes the following steps: (1) Construct a standardized oil-water interface molecular model containing the characteristic components of the target shale oil. The model includes an oil phase, a water phase, and a layer of asphaltene and resin molecules located at the oil-water interface. The demulsifier molecules to be evaluated are introduced into the system. (2) Molecular dynamics simulations were performed on the system containing different demulsifiers constructed in step (1) at two different temperatures to obtain the equilibrium trajectory of the system; (3) Calculate the difference ∆E between the interfacial binding energy per unit area between the demulsifier and the asphalt / collagen from the equilibrium trajectory obtained in step (2), as well as the thermal diffusion enhancement rate γ of the asphalt and colloidal molecules. (4) The microscopic indices ∆E and γ obtained in step (3) are correlated with the corresponding demulsifier dehydration rate measured by macroscopic bottle test, and a demulsifier performance prediction criterion with high ∆E (absolute value) and high γ value as the core is established. Based on this criterion, demulsifiers are classified and compounded.

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