Modeling Particle Sedimentation in Drilling Fluids using Gaussian Process Regression: A Computational Approach for...

Sedimentation is a fundamental unit operation in the oil industry. For characterizing this phenomenon, understanding the concentration and velocity profiles of particles is essential. Simulating this process requires integrating phenomenological (differential) models with constitutive (algebraic) equations. The inherent complexity of these models often makes simulation time a limiting factor for practical applications. This study aims to utilize Gaussian Process Regression to regularize experimental data points and predict the behavior of volumetric concentration profiles of solids in a particle sedimentation model in Br-Mul drilling fluid, used in the petroleum industry. Concentration was monitored over a 500-day period as a function of height and time, using the Gamma-ray attenuation technique. The numerical results obtained demonstrate that the proposed methodology was able to achieve good estimates for the concentration profiles at different points in the monitored domain. However, it is emphasized that the fluctuation of experimental points can result in physically unfeasible profiles. Finally, it is important to note that the computational cost required for this approach is significantly lower than that typically required by algebraic-differential model simulations.