Data-Driven Discovery of High-Performance EOR Surfactants: Coupling Molecular Dynamics with Active Learning
4 affiliations
- 1Centro de Ciências Naturais e Humanas (CCNH) − Universidade Federal do ABC , Avenida dos Estados, 5001 , , ,
- 2Universidade Federal Fluminense , , , ,
- 3Universidade Federal Fluminense , , ,
- 4Brazilian Center for Research in Energy and Materials (CNPEM) , , Lauro Vannucci, 1020 , , ,
Abstract
Chemical Enhanced Oil Recovery (EOR) is a central route to maximizing hydrocarbon recovery from mature reservoirs. Although its field-scale success depends on several factors─including surfactant cost, adsorption and retention on the reservoir rock, solubility, and phase behavior─achieving a low oil–water interfacial tension (IFT) is a key enabling requirement. However, the design of surfactants meeting this requirement is impeded by the vast combinatorics of the chemical space and the prohibitive cost of traditional trial-and-error screening. In this study, we present a synergistic materials informatics framework that integrates Machine Learning (ML) and Molecular Dynamics (MD) simulations within an Active Learning (AL) workflow to accelerate surfactant discovery. Unlike static screening approaches, our methodology utilizes MD simulations as a high-fidelity oracle, iteratively labeling a subset of candidates to update a regressor, enabling the identification of high-performance surfactants from the PubChem database. Through this workflow, we identified Dodecylammonium Chloride (DDACl) as a top candidate; subsequent high-fidelity MD simulations confirmed a very low IFT at maximum surface coverage, outperforming widely used analogs such as Cetyltrimethylammonium Bromide (CTAB). We validated these computational findings through experimental tensiometry, confirming the efficacy of DDACl in reducing IFT in several oils with varying API gravities. The convergence of theoretical predictions with experimental results underscores the power of this data-driven framework to overcome the “small data” bottleneck in materials science, offering a scalable pathway for the data-driven discovery of surfactants with low oil–water IFT as candidates for EOR formulation development.
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BibTeX
@article{veiga2026datadriven,
title = {{Data-Driven Discovery of High-Performance EOR Surfactants: Coupling Molecular Dynamics with Active Learning}},
author = {Eduardo Teixeira de Aguiar Veiga and Ednilsom Orestes and Renato D. Cunha and Paula Homem-de-Mello and Conny Cerai Ferreira and James Moraes de Almeida},
journal = {ACS Omega},
year = {2026},
month = nov,
doi = {10.1021/acsomega.6c10914},
url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c10914/5466304/Data-Driven-Discovery-of-High-Performance-EOR},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Data-Driven Discovery of High-Performance EOR Surfactants: Coupling Molecular Dynamics with Active Learning
AU - Eduardo Teixeira de Aguiar Veiga
AU - Ednilsom Orestes
AU - Renato D. Cunha
AU - Paula Homem-de-Mello
AU - Conny Cerai Ferreira
AU - James Moraes de Almeida
PY - 2026
DA - 2026/11/09
JO - ACS Omega
DO - 10.1021/acsomega.6c10914
UR - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c10914/5466304/Data-Driven-Discovery-of-High-Performance-EOR
AB - Chemical Enhanced Oil Recovery (EOR) is a central route to maximizing hydrocarbon recovery from mature reservoirs. Although its field-scale success depends on several factors─including surfactant cost, adsorption and retention on the reservoir rock, solubility, and phase behavior─achieving a low oil–water interfacial tension (IFT) is a key enabling requirement. However, the design of surfactants meeting this requirement is impeded by the vast combinatorics of the chemical space and the prohibitive cost of traditional trial-and-error screening. In this study, we present a synergistic materials informatics framework that integrates Machine Learning (ML) and Molecular Dynamics (MD) simulations within an Active Learning (AL) workflow to accelerate surfactant discovery. Unlike static screening approaches, our methodology utilizes MD simulations as a high-fidelity oracle, iteratively labeling a subset of candidates to update a regressor, enabling the identification of high-performance surfactants from the PubChem database. Through this workflow, we identified Dodecylammonium Chloride (DDACl) as a top candidate; subsequent high-fidelity MD simulations confirmed a very low IFT at maximum surface coverage, outperforming widely used analogs such as Cetyltrimethylammonium Bromide (CTAB). We validated these computational findings through experimental tensiometry, confirming the efficacy of DDACl in reducing IFT in several oils with varying API gravities. The convergence of theoretical predictions with experimental results underscores the power of this data-driven framework to overcome the “small data” bottleneck in materials science, offering a scalable pathway for the data-driven discovery of surfactants with low oil–water IFT as candidates for EOR formulation development.
SN - 2470-1343
ER -