Effects of inhibitor concentration and thermodynamic conditions on n-octylphenol-asphaltene molecular behaviours

Ghamartale, Ali; Zendehboudi, Sohrab; Rezaei, Nima; Chatzis, Ioannis

doi:10.1016/j.molliq.2021.116897

Cited by 11 publications

(9 citation statements)

References 62 publications

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“…The MD approach can determine thermodynamic properties, reaction kinetics, and fluid properties in the bulk and at the interface dynamically. 37 Recently, different MD tools such as GROMACS, LAMMPS, and Materials Studio have been used to reveal interaction mechanisms in engineering and science cases. In this study, GROMACS was employed for the MD simulation.…”

Section: Methodology and Computational Approachmentioning

confidence: 99%

“…MD simulation is a useful tool for studying biochemical, biomedical, physical, and chemical mechanisms. The MD approach can determine thermodynamic properties, reaction kinetics, and fluid properties in the bulk and at the interface dynamically . Recently, different MD tools such as GROMACS, LAMMPS, and Materials Studio have been used to reveal interaction mechanisms in engineering and science cases.…”

Section: Methodology and Computational Approachmentioning

confidence: 99%

“…Three scenarios, including (1) no inhibitor, (2) addition of 7 wt % of OP, and (3) addition of 7 wt % of IL, are considered for this series. The inhibitor concentration is selected based on the literature review, ,− and it is realistic since n -heptane, a strong asphaltene precipitator, is the only carrying fluid for the asphaltene. The second set of simulations is the same as the first set but A3 is used instead of A2.…”

Section: Methodology and Computational Approachmentioning

confidence: 99%

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Control of Asphaltene Deposition by Chemical Inhibitors in Calcite Pore: Molecular Dynamics Approach

Ghamartale

Zendehboudi

Mohamadi‐Baghmolaei

2022

Ind. Eng. Chem. Res.

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Asphaltene deposition is a major problem during oil production and transportation that imposes extra treatment costs and reduces oil production. Historically, various chemical inhibitors have been developed to resolve the asphaltene deposition issue. However, the inhibitors are usually effective for a specific type of crude oil and asphaltene since asphaltene's nature is different for various oil samples. To develop a proper chemical inhibitor, the interaction between the inhibitor and asphaltene needs to be explored. This work employs a molecular dynamics (MD) simulation strategy to study asphaltene deposition on a calcite surface considering chemical inhibitors. Two asphaltene structures with potential to form hydrogen bond (A2) and without potential to form hydrogen bond (A3) are considered in this study. The selected inhibitors, including noctylphenol (OP) and 1-butyl-3-methylimidazolium chloride (as an ionic liquid (IL)), can form van der Waals, Coulomb, and hydrogen bonds with asphaltene molecules. The results show that the OP reduces the asphaltene aggregation by attaching to the asphaltene through hydrogen bonds. In the presence of OP, the Lennard-Jones (LJ) and Coulomb energies between asphaltene (A2) and calcite are reduced by 400 and 1000 kJ/mol units, leading to the asphaltene deposition reduction by adsorbing on the calcite surface and providing a hindrance layer. The IL is able to cope with the quadrupole−quadrupole interaction between asphaltene polyaromatic cores and reduce the asphaltene face-to-face aggregation. However, IL cannot provide a hindrance layer near the calcite surface since it does not have a long hydrocarbon tail. Therefore, the combination of inhibitors can benefit the inhibition process as both prevention mechanisms will be active. The 3:1 OP−IL ratio shows the optimum efficiency. At this ratio, inhibitors reduce the aggregation from 20 to less than 10 and the deposition rate from 1 to 0.8 compared to the case without the inhibitors. Also, the deposited aggregates have low compaction with a spherical shape, which is easy to dislodge in a dynamic situation. This research aims to demystify the asphaltene−inhibitor behaviors during asphaltene deposition, which can be a useful practice for designing of future chemical inhibitors for flow assurance issues.

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Section: Methodology and Computational Approachmentioning

confidence: 99%

Section: Methodology and Computational Approachmentioning

confidence: 99%

Section: Methodology and Computational Approachmentioning

confidence: 99%

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Control of Asphaltene Deposition by Chemical Inhibitors in Calcite Pore: Molecular Dynamics Approach

Ghamartale

Zendehboudi

Mohamadi‐Baghmolaei

2022

Ind. Eng. Chem. Res.

Self Cite

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“…Given that their observations lay outside the domain of the Yen–Mullins model, they consequently argued that this colloidal model, even though it is capable of describing the aggregation process of standard asphaltenes, might be a particular case of the more general supramolecular model, as proposed by Gray et al, which is better suited to address the complete chemical diversity of asphaltenes. Furthermore, in a few studies, MD simulations have also been deployed to investigate the inhibitor action of chemical additives such as dodecylbenzenesulfonic acid, limonene, n - octylphenol, − and a series of polymers (two succinimide-based structures and one maleic anhydride) on asphaltene aggregation. Finally, Headen et al have demonstrated that MD aggregation simulations for single asphaltene model systems qualitatively reproduce neutron total scattering data …”

Section: Introductionmentioning

confidence: 99%

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

Pétuya¹,

Punase

Bosoni³

et al. 2023

ACS Omega

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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.

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“…23 Given that their observations lay outside the domain of the "Yen-Mullins" model, they consequently argued that this colloidal model, even though it is capable of describing the aggregation process of standard asphaltenes, might be a particular case of the more general supramolecular model, as proposed by Gray et al, 13 which is better suited to address the complete chemical diversity of asphaltenes. Furthermore, in a few studies, MD simulations have also been deployed to investigate the inhibitor action of chemical additives such as dodecylbenzenesulfonic acid, 25 limonene, 17 n-octylphenol, [26][27][28] and a series of polymers (two succinimide-based structures and one maleic anhydride), 29 on asphaltene aggregation. Finally, Headen et al have demonstrated that MD aggregation simulations for monodisperse asphaltene systems qualitatively reproduce neutron total scattering data.…”

Section: Introductionmentioning

confidence: 99%

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

Pétuya¹,

Punase

Bosoni³

et al. 2022

Preprint

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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.

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Effects of inhibitor concentration and thermodynamic conditions on n-octylphenol-asphaltene molecular behaviours

Cited by 11 publications

References 62 publications

Control of Asphaltene Deposition by Chemical Inhibitors in Calcite Pore: Molecular Dynamics Approach

Control of Asphaltene Deposition by Chemical Inhibitors in Calcite Pore: Molecular Dynamics Approach

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

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

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