Time series data from past drilling operations are an under-utilized resource in oil-companies. Time series record processes in the well, effects of machinery on the rig, and presents an opportunity to improve upon the models underlying drilling simulations as well as alarm systems.

We present a proof-of-concept for reducing false kick alarms during drilling by combining a physical model with techniques from artificial intelligence. We show that artificial intelligence can be used to learn from the experience implicit in time series data. It then corrects for limitations in the physical model, which results in a more accurate prediction of the mud flow out of the well, and subsequently fewer false kick alarms.

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