Water affects almost every operation in the exploration and production (E&P) industry, with its properties important to flow assurance, to three-phase flow pressure/volume/temperature (PVT) modeling, and for fluid compatibility purposes across well construction, stimulation, and production operations. Until now, time-intensive laboratory tests or cumbersome third-party simulators were required to extract physicochemical properties. Here, a family of machine-learning-based reduced-order models (ROM), trained on rigorous first-principle thermodynamic simulation results, is presented.

Approximately 90,000 representative produced-water samples were generated using the United States Geological Survey (USGS) Produced Waters Geochemical Database (Blondes et al. 2019), with systematic variation of the concentrations of 14 common ions. A training data set of 1 million rows was constructed, further varying temperatures and pressures using broad ranges (50-400°F and 14.7-20,000 psi). Thermodynamic simulations were used to generate a data set with more than 500 parameters, including speciation; physicochemical properties such as density, thermal conductivity, heat capacity, and salinity; and notably, the scaling potential for 11 common oilfield scale-forming minerals. More than 20 machine-learning algorithms were screened using cross-validation, and boosted decision trees were found to provide the best accuracy. The CatBoost algorithm (Prokhorenkova et al. 2018) was selected and further optimized. Model validation using unseen data showed relative errors of less than 1% for the majority of predicted properties, which is remarkable for such a complex multicomponent and multiphase system. Simulation details, modeling, and validation results are discussed.

Trained and optimized ROMs can be incorporated in any workflow that depends on water property predictions. As a demonstration, a web application, Water Digital Avatar, was built from these ROMs to quickly and accurately process predictions of water properties and scaling potential on the basis of the entered water composition and desired conditions. The streamlined workflow provides users with model predictions in tabulated and graphical forms for analysis within the web application or offline by means of a downloaded spreadsheet.

The developed ROMs that predict water properties enable automated decision making and improve water management workflows. The presented approach can be further extended to other oilfield, chemical, and chemical engineering applications.

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