水几乎影响着勘探和生产行业的每一项作业。到目前为止，需要耗时的实验室测试或繁琐的第三方模拟器来提取物理化学特性。在完整的论文中，提出了一系列基于机器学习的降阶模型（ROM），这些模型是根据严格的第一原理热力学模拟结果进行训练的。所开发的可预测水特性的 ROM 可实现自动化决策并改进水管理工作流程。

Water affects almost every operation in the exploration and production industry. Until now, time-intensive laboratory tests or cumbersome third-party simulators were required to extract physicochemical properties. In the complete paper, a family of machine-learning-based reduced-order models (ROMs) trained on rigorous first-principle thermodynamic simulation results is presented. The developed ROMs that predict water properties enable automated decision-making and improve water-management work flows.