An Ensemble Machine Learning Model for Predictive Maintenance on Water Injection Pumps in the Oil and Gas Industry

Abstract

The effective operation of water injection pumps is vital for enhancing oil recovery in the oil and gas industry. To ensure optimal pump performance and prevent unplanned downtime, this study focused on implementing predictive maintenance strategies. We began by identifying five critical operational parameters—Seal Pressure 1, Seal Pressure 2, Vibration Data for the Drive End (VIB DE), Vibration Data for the Non-Drive End (VIB NDE), and Ampere. These parameters were monitored and analyzed to evaluate their impact on pump performance and maintenance needs. To achieve this, we applied three machine learning algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LGBM), and Random Forest. Each algorithm was independently trained and tested on the dataset corresponding to each operational parameter. We assessed their performance using key accuracy metrics, including R squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Following this, we developed an Ensemble model, combining the predictive outputs of XGBoost, LGBM, and Random Forest. The Ensemble model was then applied to the same parameters to evaluate its ability to address the limitations observed in standalone models. The results demonstrated that the Ensemble model consistently delivered superior performance, achieving lower RMSE and MAE values and higher R squared coefficients across all parameters. This study culminates in the validation of the Ensemble model as a robust and reliable approach for predictive maintenance. By leveraging the strengths of multiple algorithms, the Ensemble model offers significant improvements in accuracy and reliability, contributing to more effective maintenance systems for the oil and gas industry.