Abstract

Using machine learning to predict maintenance schedules for crude oil pipelines is crucial for enhancing efficiency and minimizing disruptions in the oil and gas sector. Our research explores the effectiveness of machine learning algorithms in this context, with a specific focus on using oil flow rate as a primary predictor. Machine learning models, when trained with a variety of inspection data, can accurately predict flow rate, thus improving maintenance planning. Several pipeline scenarios were analysed, and Python library was used for dataset augmentation. The study shows a correlation between variations in the buildup deposits and flow rate in the pipeline, indicating that the flow rate gives an indication for determining the needs for maintenance. Specifically, higher flow rate aloud longer intervals between maintenance activities like pigging, while lower flow rate could indicate there is accumulation of deposit which necessitating intervention. Ensembled machine learning models was train, variation in performance were observed. Gradient Boosting and XGBoost Regressor show best performers with lower values for MSE, RMSE, and MAE, and higher R² scores compare to the Support Vector Regressor. The result shows Gradient Boosting has MSE of 0.000005, RMSE of 0.002259, MAE of 0.000968, and an R² of 0.997259, follow by XGBoost Regressor with MSE of 0.000005, an RMSE of 0.002269, an MAE of 0.000922, and an R² of 0.997234. while Support Vector Regressor indicate the least performance, with MSE of 0.002868, RMSE of 0.053554, MAE of 0.046311, and an R² of -0.540765. The findings of the study emphasize the necessity of choosing machine learning algorithms that are appropriately suited to the features of the dataset and the task. The findings highlight the importance of selecting machine learning algorithms that are more suitable to the features of the dataset and the task.