Brine-Oil Interfacial Tension Modeling: Assessment of Machine Learning Techniques Combined with Molecular Dynamics

Review of the Interfacial Structure and Properties of Surfactants in Petroleum Production and Geological Storage Systems from a Molecular Scale Perspective

Surfactants play a crucial role in tertiary oil recovery by reducing the interfacial tension between immiscible phases, altering surface wettability, and improving foam film stability. Oil reservoirs have high temperatures and high pressures, making it difficult and hazardous to conduct lab experiments. In this context, molecular dynamics (MD) simulation is a valuable tool for complementing experiments. It can effectively study the microscopic behaviors (such as diffusion, adsorption, and aggregation) of the surfactant molecules in the pore fluids and predict the thermodynamics and kinetics of these systems with a high degree of accuracy. MD simulation also overcomes the limitations of traditional experiments, which often lack the necessary temporal-spatial resolution. Comparing simulated results with experimental data can provide a comprehensive explanation from a microscopic standpoint. This article reviews the state-of-the-art MD simulations of surfactant adsorption and resulting interfacial properties at gas/oil-water interfaces. Initially, the article discusses interfacial properties and methods for evaluating surfactant-formed monolayers, considering variations in interfacial concentration, molecular structure of the surfactants, and synergistic effect of surfactant mixtures. Then, it covers methods for characterizing microstructure at various interfaces and the evolution process of the monolayers' packing state as a function of interfacial concentration and the surfactants' molecular structure. Next, it examines the interactions between surfactants and the aqueous phase, focusing on headgroup solvation and counterion condensation. Finally, it analyzes the influence of hydrophobic phase molecular composition on interactions between surfactants and the hydrophobic phase. This review deepened our understanding of the micro-level mechanisms of oil displacement by surfactants and is beneficial for screening and designing surfactants for oil field applications.

Machine Learning-Based Interfacial Tension Equations for (H2 + CO2)-Water/Brine Systems over a Wide Range of Temperature and Pressure

Large-scale underground hydrogen storage (UHS) plays a vital role in energy transition. H2-brine interfacial tension (IFT) is a crucial parameter in structural trapping in underground geological locations and gas-water two-phase flow in subsurface porous media. On the other hand, cushion gas, such as CO2, is often co-injected with H2 to retain reservoir pressure. Therefore, it is imperative to accurately predict the (H2 + CO2)-water/brine IFT under UHS conditions. While there have been a number of experimental measurements on H2-water/brine and (H2 + CO2)-water/brine IFT, an accurate and efficient (H2 + CO2)-water/brine IFT model under UHS conditions is still lacking. In this work, we use molecular dynamics (MD) simulations to generate an extensive (H2 + CO2)-water/brine IFT databank (840 data points) over a wide range of temperature (from 298 to 373 K), pressure (from 50 to 400 bar), gas composition, and brine salinity (up to 3.15 mol/kg) for typical UHS conditions, which is used to develop an accurate and efficient machine learning (ML)-based IFT equation. Our ML-based IFT equation is validated by comparing to available experimental data and other IFT equations for various systems (H2-brine/water, CO2-brine/water, and (H2 + CO2)-brine/water), rendering generally good performance (with R2 = 0.902 against 601 experimental data points). The developed ML-based IFT equation can be readily applied and implemented in reservoir simulations and other UHS applications.

Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance analysis

Interfacial tension (IFT) is a key physical property that affects various processes in the oil and gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability. Accurate prediction of IFT is essential for optimizing these processes and increasing their efficiency. This article compares the performance of six machine learning models, namely Support Vector Regression (SVR), Random Forests (RF), Decision Tree (DT), Gradient Boosting (GB), Catboosting (CB), and XGBoosting (XGB), in predicting IFT between oil/gas and oil/water systems. The models are trained and tested on a dataset that contains various input parameters that influence IFT, such as gas-oil ratio, gas formation volume factor, oil density, etc. The results show that SVR and Catboost models achieve the highest accuracy for oil/gas IFT prediction, with an R-squared value of 0.99, while SVR outperforms Catboost for Oil/Water IFT prediction, with an R-squared value of 0.99. The study demonstrates the potential of machine learning models as a reliable and resilient tool for predicting IFT in the oil and gas industry. The findings of this study can help improve the understanding and optimization of IFT forecasting and facilitate the development of more efficient reservoir management strategies.

Synergistic Interaction of Hyperbranched Polyglycerols and Cetyltrimethylammonium Bromide for Oil/Water Interfacial Tension Reduction: A Molecular Dynamics Study

Applying surfactants to reduce the interfacial tension (IFT) on water/oil interfaces is a proven technique. The search for new surfactants and delivery strategies is an ongoing research area with applications in many fields such as drug delivery through nanoemulsions and enhanced oil recovery. Experimentally, the combination of hyperbranched polyglycerol (HPG) with cetyltrimethylammonium bromide (CTAB) substantially reduced the observed IFT of oil/water interface, 0.9 mN/m, while HPG alone was 5.80 mN/m and CTAB alone IFT was 8.08 mN/m. Previous simulations in an aqueous solution showed that HPG is a surfactant carrier. Complementarily, in this work, we performed classical molecular dynamics simulations on combinations of CTAB and HPG with one aliphatic chain to investigate further the interaction of this pair in oil interfaces and propose the mechanism of IFT decrease. Basically, from our results, one can observe that the IFT reduction comes from a combination of effects that have not been observed for other dual systems: (i) Due to the CTAB-HPG strong interaction, a weakening of their specific and isolated interactions with the water and oil phases occurs. (ii) Aggregates enlarge the interfacial area, turning it into a less ordered interface. (iii) The spread of individual molecules charge profiles leads to the much lower interfacial tension observed with the CTAB+HPG systems.

Interfacial Tension-Temperature-Pressure-Salinity Relationship for the Hydrogen-Brine System under Reservoir Conditions: Integration of Molecular Dynamics and Machine Learning

Hydrogen (H2) underground storage has attracted considerable attention as a potentially efficient strategy for the large-scale storage of H2. Nevertheless, successful execution and long-term storage and withdrawal of H2 necessitate a thorough understanding of the physical and chemical properties of H2 in contact with the resident fluids. As capillary forces control H2 migration and trapping in a subsurface environment, quantifying the interfacial tension (IFT) between H2 and the resident fluids in the subsurface is important. In this study, molecular dynamics (MD) simulation was employed to develop a data set for the IFT of H2-brine systems under a wide range of thermodynamic conditions (298-373 K temperatures and 1-30 MPa pressures) and NaCl salinities (0-5.02 mol·kg-1). For the first time to our knowledge, a comprehensive assessment was carried out to introduce the most accurate force field combination for H2-brine systems in predicting interfacial properties with an absolute relative deviation (ARD) of less than 3% compared with the experimental data. In addition, the effect of the cation type was investigated for brines containing NaCl, KCl, CaCl2, and MgCl2. Our results show that H2-brine IFT decreases with increasing temperature under any pressure condition, while higher NaCl salinity increases the IFT. A slight decrease in IFT occurs when the pressure increases. Under the impact of cation type, Ca2+ can increase IFT values more than others, i.e., up to 12% with respect to KCl. In the last step, the predicted IFT data set was used to provide a reliable correlation using machine learning (ML). Three white-box ML approaches of the group method of data handling (GMDH), gene expression programming (GEP), and genetic programming (GP) were applied. GP demonstrates the most accurate correlation with a coefficient of determination (R2) and absolute average relative deviation (AARD) of 0.9783 and 0.9767%, respectively.

Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning

Molecular dynamics simulation is an indispensable tool for understanding the collective behavior of atoms and molecules and the phases they form. Statistical mechanics provides accurate routes for predicting macroscopic properties as time-averages over visited molecular configurations - microstates. However, to obtain convergence, we need a sufficiently long record of visited microstates, which translates to the high-computational cost of the molecular simulations. In this work, we show how to use a point cloud-based deep learning strategy to rapidly predict the structural properties of liquids from a single molecular configuration. We tested our approach using three homogeneous liquids with progressively more complex entities and interactions: Ar, NO, and H₂O under varying pressure and temperature conditions within the liquid state domain. Our deep neural network architecture allows rapid insight into the liquid structure, here probed by the radial distribution function, and can be used with molecular/atomistic configurations generated by either simulation, first-principle, or experimental methods.

Surfactant-Controlled Mobility of Oil Droplets in Mineral Nanopores

Polymer flooding is one of the widely used enhanced oil recovery (EOR) methods. However, tuning polymer properties to achieve improved performance in porous mineral rocks of diverse oil reservoirs remains one of the challenges of EOR processes. Here, we use molecular dynamics (MD) simulations to examine decane/water mixtures with surfactant additives in calcite and kaolinite mineral nanopores and characterize surfactant properties associated with increased fluid mobility and improved wettability in planar and constricted nanopore geometries. Cetyltrimethylammonium chloride (CTAC) and sodium dodecyl sulfate (SDS) surfactants are found to modulate the contact angles of decane droplets and reduce the decane density on mineral surfaces. CTAC can enhance and unblock the flow of decane droplets through narrowing nanopores with constricted geometries while aiding in decane droplet shape deformation, whereas SDS leads to decane droplets stalling in front of constrictions in nanopores. We hypothesize that the inability of the cationic CTAC headgroup to form hydrogen bonds is one of the key factors leading to enhanced CTAC-coated decane flow through constricted nanopores. The obtained molecular view of equilibrium and dynamic properties of complex fluids typical of oil reservoirs can provide a basis for the future design of new molecules for EOR processes.