Generating downhole fluid compositions for wellbore operations using machine learning

Abstract

Some examples described herein relate to producing an optimized composition of a downhole drilling fluid. For example, a system can execute an iterative optimization process to determine an optimized composition of downhole drilling fluid that satisfies at least one objective function and matches a received set of target fluid properties. Each iteration of the iterative optimization process can involve: selecting a mixture of fluid components for the downhole drilling fluid from a search space, providing the selected mixture of fluid components as input to a trained machine-learning model, receiving a set of predicted fluid properties for the mixture of fluid components as output from the trained machine-learning model, and determining whether the set of predicted fluid properties matches the set of target fluid properties. The system can transmit a control signal to a mixing subsystem for causing the mixing subsystem to produce the optimized composition of the downhole drilling fluid.

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