Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Molecular dynamics simulations have been employed to
investigate
the effect of molecular 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, single asphaltene model simulations
have shown a broad range of aggregation behaviors, 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 mixtures have highlighted the complex
and diverse effects of molecular polydispersity on the aggregation
process of asphaltene. Simulations yielded both antagonistic and synergistic
effects mediated by the trigger or facilitator action of specific
asphaltene model molecules. These findings illustrate the necessity
of accounting for molecular polydispersity when studying the asphaltene
aggregation process and have permitted establishing a robust protocol
for the in silico evaluation of the performance of
asphaltene inhibitors, as illustrated for the case of a nonylphenol
resin.