Oilfield chemistry

Reduced-Order Models Blend Chemistry, Machine Learning for Water-Property Analysis

This paper presents a family of machine-learning-based reduced-order models trained on rigorous first-principle thermodynamic simulation results to extract physicochemical properties.

Prototype of the Web application that predicts produced-water properties.
Fig. 1—Prototype of the Web application that predicts produced-water properties.
SPE 213869.

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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