Autodrill: framework autoML para previsão da taxa de perfuração em poços de petróleo

Abstract

Predicting the Rate of Penetration (ROP) plays a central role in optimizing operations in the oil and gas industry, directly influencing operational efficiency and drilling-related costs. However, conventional machine learning models have limitations in this context due to the strong non-linearity of the variables involved and, above all, the high lithological heterogeneity and low reliability of downhole sensors, resulting in noisy and gapped time series. This work proposes AutoDrill, an Automated Machine Learning (AutoML) framework specifically designed for the drilling domain. In contrast to generalist approaches, AutoDrill incorporates a preprocessing pipeline specialized in drilling signals (including adaptive smoothing, transient correction, and data interpolation) coupled with an automated hyperparameter search and tuning mechanism. The experimental evaluation was conducted on various datasets and geological scenarios, using records from three offshore repositories (Equinor Volve, USROP, and NEATS NOPIMS). The performance of AutoDrill was compared to the AutoGluon and H2O AutoML frameworks, considered representative of the state of the art in AutoML. The results indicate that AutoDrill exhibits consistent and competitive performance, achieving an average coefficient of determination (R2) of 0.91 across all analyzed scenarios, particularly in environments characterized by high heterogeneity and noise, such as in the case of NEATS NOPIMS. In this specific scenario, AutoDrill significantly outperformed the reference frameworks, reducing the mean absolute error (MAE) by up to 80% and raising the R2 from levels below 0.20 to 0.94. In conclusion, the systematic incorporation of domain knowledge into the machine learning automation cycle is crucial for the robustness of real-time prediction. From this perspective, AutoDrill presents itself as a practical, scalable, and domain-oriented approach for ROP prediction, contributing to the advancement of machine learning techniques in petroleum engineering.