Authors: V. Y. Hon | I. B. M. Saaid | I. C. H. Chai | Estelle Deguillard | J. v. Male | J. W. Handgraaf
Source: Offshore Technology Conference Asia (article, 2026-03-30)
Field: Chemistry — Organic Chemistry

Harnessing Automation for EOR Surfactant Design: A Patent-Driven Approach Abstract Automation has transformed research-intensive industries, with pharmaceutical and material sciences leading the adoption of AI-driven workflows to accelerate development. Leveraging on these advancements, this study introduces an automated methodology for surfactant design, integrating computational chemistry simulations with patent analysis for enhanced oil recovery (EOR) application. By assessing interfacial properties of surfactant generated microemulsion, this study provides insights into the relationship between molecular composition, structural modifications, and oil recovery potential, shifting the laboratory surfactant design process towards computational optimization. The methodology involves an automated workflow incorporating patent search, molecular structure generation, molecular dynamics simulations, and data analysis to streamline surfactant design. Global database patent search and analysis are used to extract potential surfactant structures, which are then converted into SMILES representations for molecular dynamics simulation evaluation. The simulation employs a physics-based model to assess the EOR microemulsion phase behavior and bending rigidity. The simulations continue to refine molecular compositions based on interfacial stability and bending properties. This iterative computational approach enables rapid molecule generation within hours, with refinement cycles allowing structural optimizations based on early data assessment. Patent analysis identified three promising surfactant types—amphoteric, multicarboxylate, and Gemini variants—that were evaluated computationally for interfacial stability and microemulsion phase behavior. 25 potential structures were generated across the three surfactant types. Upon evaluation, only one structure met synthesis criteria for experimental validation. This has eliminated significant amount of resources spent in assessing numerous less potential surfactant. The automated workflow resulted in a 90% reduction in surfactant design time compared to conventional laboratory testing, completing molecular assessments in approximately 35 hours computational time instead of a month laboratory synthesis per surfactant. This study demonstrates the challenges of designing surfactants from patent-derived molecular structures while showcasing how automation can significantly accelerate screening and optimization. By integrating patent-driven molecule generation with computational chemistry simulations, surfactant design process is enhanced, shifting away from traditional empirical experimentation. This study highlights automation's potential to refine surfactant structures in a scalable manner. Further integration with AI could refine molecular design by bridging theoretical predictions with real-world synthesis feasibility, creating a next-generation platform for chemical innovation. Pulmonary surfactant ; Microemulsion ; Workflow ; Molecular dynamics ; Automation ; Stability (learning theory) ; Process (computing) Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Enhanced Oil Recovery Techniques