Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative...

Area of Science:

• Petroleum science
• Computational chemistry
• Materials science

Background:

• Asphaltene aggregation significantly impacts oil recovery and processing.
• Understanding molecular polydispersity is crucial for predicting asphaltene behavior.
• Current models often oversimplify the complex nature of asphaltene mixtures.

Purpose of the Study:

• To investigate the influence of molecular polydispersity on asphaltene aggregation using molecular dynamics simulations.
• To develop a systematic approach for studying diverse asphaltene blends.
• To establish a computational protocol for evaluating asphaltene inhibitors.

Main Methods:

• Utilized unsupervised machine learning (clustering) to select representative asphaltene model molecules.
• Performed single asphaltene and mixture molecular dynamics simulations.
• Analyzed aggregation behavior based on molecular structural features (aromatic core, aliphatic chains, heteroatoms).

Main Results:

• Single asphaltene simulations showed varied aggregation driven by structural characteristics.
• Mixture simulations revealed complex antagonistic and synergistic effects of polydispersity.
• Specific asphaltene molecules acted as triggers or facilitators in aggregation.

Conclusions:

• Molecular polydispersity significantly and complexly affects asphaltene aggregation.
• Accounting for molecular diversity is essential for accurate aggregation studies.
• Developed a robust *in silico* protocol for evaluating asphaltene inhibitors, demonstrated with nonylphenol resin.