Previsão de Taxa de Perfuração em Poços de Petróleo Offshore Utilizando Aprendizado de Máquina

• André Branco UFRJ
• Janaína Gomide UFRJ

DOI: https://doi.org/10.5753/eniac.2021.18279

Abstract

The rate of perforation (ROP) of an oil well is a very important metric to control, as it affects well productivity, bit wear, and well and operational security. This work evaluates the use of Machine Learning algorithms for predicting ROP in offshore oil wells as an alternative for other models traditionally used by the industry. Random forest models were evaluated for different hyperparameters for 4 different offshore oilwells in Santos Basin's pre-salt. In general, random forest models performed the best for each well, but there was no optimal combination of hyperparameters between all wells.

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