This research aims to develop a fluid-advisory system that provides recommendations for the optimal amounts of chemical additives needed to maintain desired fluid properties in various drilling-fluid systems. Building on the foundation laid by the fluid-advisory system for water-based fluids described elsewhere in the literature, this project expands its scope to include both water- and oil-based muds. To achieve this goal, a machine-learning model is created to predict target fluid properties, with separate models developed for water- and oil-based muds. An integrated optimization framework determines the ideal quantities of chemical additives.
Methodology
The fluid-advisory system calculates the optimal amount of a chemical additive needed to adjust a fluid property, such as mud weight, rheology, pH, hardness, chloride concentration, oil/water ratio, concentration of low-gravity solids, excess lime, and electrical stability.
