Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under h...Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions.Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue,the complex interplay among polymer topology,charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging.In this work,we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures.Guided by practical molecular design strategies,the topological features (grafting density,side-chain length) and functional group-related features(copolymerization ratio,hydrophilic-hydrophobic balance) are encoded into a multidimensional design space.By integrating dissipative particle dynamics simulations with particle swarm algorithm,the framework efficiently explores the design space and identifies non-intuitive,high-performing polymer structure.The optimized polymer achieves a 12%enhancement in viscosity,attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation.This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.展开更多
基金supported by the Key Technologies R&D Program of China National Offshore Oil Corporation(No.KJGG2021-0504).
文摘Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions.Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue,the complex interplay among polymer topology,charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging.In this work,we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures.Guided by practical molecular design strategies,the topological features (grafting density,side-chain length) and functional group-related features(copolymerization ratio,hydrophilic-hydrophobic balance) are encoded into a multidimensional design space.By integrating dissipative particle dynamics simulations with particle swarm algorithm,the framework efficiently explores the design space and identifies non-intuitive,high-performing polymer structure.The optimized polymer achieves a 12%enhancement in viscosity,attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation.This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.
