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.
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