Molecular dynamics simulations of asphaltene aggregation: machine learning identification of representative molecules, polydispersity and inhibitor performance

Molecular Dynamics simulations have been employed to investigate the effect of polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, monodisperse asphaltene simulations have shown a broad range of aggregation behavior, driven by their structural features: size of the aromatic core, length of the aliphatic chains and presence of heteroatoms. Then, the combination of these model molecules in a series of polydisperse mixtures have highlighted the complex and diverse effects of polydispersity on the aggregation process of asphaltene, which yielded both antagonistic, synergistic and seed effects. These findings illustrate the necessity of accounting for polydispersity when studying the asphaltene aggregation process and have permitted to establish a robust protocol for the in-silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin.