Leveraging Machine Learning for Predicting Marginal Field Oil Well Production Using Electrical Submersible Pump Operations Data

Abstract

Electrical Submersible Pumps (ESPs) are critical components of oil well production that can be evaluated for their longevity through daily performance predictions. Traditional predictive methodologies have failed to handle the complexity of dynamic multi-well systems, creating a demand for automated Machine Learning (ML) pipelines based on operational data. In this study, we develop a multivariate time series ESP dataset based on factors impacting the total oil production of four wells (A1-A4) in the Jay field, Malaysia. A naive baseline pipeline used a 95%/5% chronological train-test split across pure regression models and a multivariate Prophet setup. However, due to the presence of data leakage, flat-trend autocorrelation, and the inability to process mechanical well shut-ins, the result of such an approach was unsatisfactory, producing extremely poor R² scores. In order to combat poor results and to find a more optimal solution, we have created a synchronized-lag multivariate pipeline evaluated across a 5-Fold Walk-Forward Cross-Validation framework. Noise has been eliminated by cutting leading zeros from the dataset pre-production, while stationarity has been provided through first-order differencing of target values for tracking daily changes (Δ y). To ensure temporal synchronization, a strict one-day chronological lag protocol has been used across all sixteen operational engineering features, splitting them up into a historical baseline (t-1) and an operational delta change feature (t-2 to t-1). The resulting input has allowed us to turn pure regression models into time series models forced to predict changes in targets based on purely historical pump parameters. Results show that non-linear ensemble models perform significantly better than Prophet under rolling validation. The Light Gradient Boosting Machine (LGBM) proved to be the champion model, achieving a statistically significant R² score of 0.31. Feature importance analysis showed that the operational adjustments of localized wells (specifically associated Gas_A2_delta and lag_oil_1) drive downstream fluid changes the most. Against the platform capacity of 9,500 barrels per day, LGBM's MAE translates to only 4.1% daily variation forecast.