Advancing polymeric membranes for produced water treatment: From fouling mitigations to machine-learning driven design

Produced water (PW) management remains a major environmental and operational concern in the oil and gas sector. Despite its potential for reuse and resource recovery, the complex nature of PW limits the feasibility of conventional treatment approaches. Polymeric membranes (PMs) have gained attention as a promising solution due to their tunable properties and adaptability across multiple separation mechanisms. However, the performance of PMs in PW treatment is still hindered by several challenges, most notably fouling, which cascades into a wide range of issues affecting stability, rejection rates, and longevity. This review critically discusses recent advances in PM design, emphasizing mixed matrix membranes, surface modification strategies, and smart responsive systems. Comparative insights are presented, including benchmarks for oil and grease removal, dissolved organics separation, desalination, and heavy metal remediation. Surface modification strategies, including hydrophilic charged, zwitterionic, and self-cleaning and smart responsive surfaces, are critically studied in terms of their ability to enhance antifouling performance, selectivity, and long-term stability under complex PW conditions. Furthermore, the PMs' fabrication methods are highlighted, with a particular focus on their performance trade-offs, sustainability considerations, and associated challenges. However, concerns about the competitive interaction of co-occurring pollutants are underlined by a concern about the PM's performance in real PW, as current developed PMs are often tested for single pollutant feeds, highlighting the importance of PM-feed oriented design to achieve practical, data-informed, and scalable solutions for PW treatment. Furthermore, the paper introduces machine learning (ML) as a promising technology to facilitate PM optimization and design of novel polymers, saving resources and time, accompanied by a feedback-based ML framework tailored for PM design and optimization.