The utilization of synergistic mixtures of nanoparticles (NPs) and surfactants for enhanced oil recovery (EOR) has drawn increasing scientific attention. In this study, a series of coarse-grained (CG) molecular dynamics (MD) models were built to study the behaviors of NPs and surfactants in the vicinity of the oil/water interface. Hydrophilic, hydrophobic, and amphiphilic NPs were constructed to investigate the effect of hydrophobicity on the ability of NPs in term of interfacial tension (IFT) reduction. The synergistic effect of surfactants and NPs were also studied.
Surfactants and amphiphilic NPs can both accumulate at the interface of oil and water, while hydrophilic and hydrophobic NPs stay in water or oil phase. The NPs with various ratios of hydrophobic to hydrophilic domains were investigated to determine the types of NPs that result in the most IFT reduction. The comparison of IFTs indicates that amphiphilic NPs has a better ability to assist surfactants in further reducing the interfacial tension. Meanwhile, surface modification and the presence of surfactants can prevent the aggregation of NPs.
These MD simulation results allow us to figure out the physical behavior of NPs and surfactants at the oil/water interfaces. Analysis of the results can further assist the NPs synthesis for surfactant and/or surfactant-nanoparticle EOR applications in unconventional reservoirs.
Enhanced Oil Recovery (EOR) is well known for its potential to produce residual oil after the primary and secondary oil recovery. The residual oil is trapped in the narrow throat due to high capillary pressure, which is influenced by rock wettability and oil/water interfacial tension (IFT) (Wu et al., 2008). Surfactants have been widely investigated and employed in the EOR process to reduce the IFT and to alter the wettability (Sheng et al. 2015; Kamal et al., 2017; Negin et al., 2017). However, during the surfactant flooding, surfactants can adsorb onto the rock surfaces. This may result in the reduction of their concentrations, which significantly reduce the efficiency of surfactants in practical applications. The high cost of surfactants also makes this potential loss a critical issue. Many researchers have focused their studies on reducing the adsorption of surfactants by adding various materials in the chemical formulations.
The suggested algorithm consists of three primary entities: the reservoir, the wellbore, and the completion. A reservoir simulator provides the rate as a function of drawdown or bottomhole pressure (BHP), and a wellbore model is used to calculate the frictional pressure loss along the wellbore and the surface flowlines. Depending on the fluid system, the appropriate choke-flow model is used. Dynamic (time-dependent) nodal analysis ensures the continuity of pressure and rates between the wellbore and reservoir entities at every timestep. The algorithm suggests the maximum available choke that satisfies, at all times, the entire set of user-specified constraints.