Cross-Well Validated Binary Classification for ESP Failure Prediction Using Gradient Boosting

Abstract

Electric Submersible Pumps (ESPs) are a widely used artificial-lift technology in oil production, yet unplanned failures lead to costly workovers and substantial production losses. Most machine-learning approaches for ESP predictive maintenance (PdM) are evaluated using same-well validation protocols, such as random splits or k-fold cross-validation, which do not reflect deployment conditions where models must generalize to previously unseen wells and may yield overly optimistic performance estimates. This paper applies Leave-One-Well-Out (LOWO) cross-well validation as a realistic evaluation protocol and demonstrates its implications on the public SPE E-Challenge dataset (63 retained wells; 17 failure wells used in LOWO classification). We show that Remaining Useful Life (RUL) regression performs poorly under cross-well conditions, with no evaluated model exceeding R2 = 0.24. We then evaluate five classification configurations varying featurization, calibration, and ensembling strategy, with the best results reaching AUC = 0.93 and F1 = 0.72 under LOWO. These results highlight important limitations of current PdM validation practices and show that cross-well evaluation is essential for realistic assessment of predictive maintenance models.