The research focuses on evaluation of machine learning models in the context of predicting maintenance strategies within the oil and gas pipeline, with a primary emphasis on life-cycle cost analysis. The study underscores the crucial shift from traditional, time-based maintenance practices to data-driven, predictive maintenance strategies, which hold significant potential for enhancing safety, reliability, and cost-efficiency for pipeline operators. To address limitations associated with data availability, an innovative methodology is employed involving the generation and utilization of synthetic data. Through the simulation of diverse pipeline scenarios, the research successfully creates a comprehensive dataset for the prediction of maintenance strategies based on cost-benefit ratios. The experimental results provide valuable insights into the strengths and weaknesses of various machine learning models. Notably, Random Forest Classifier and Gradient Boosting Classifier emerge as top-performing models for classification tasks, also the predictions show that corrective maintenance has the highest frequency compared to other maintenance strategies. This study contributes significantly to the ongoing efforts to improve pipeline management within the oil and gas industry.

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. When trained with a variety of inspection data, machine learning models can accurately predict flow rates, thus improving maintenance planning. Several pipeline scenarios were analyzed, and the Python library was used for dataset augmentation. The study shows a correlation between variations in the buildup deposits and the oil flow rate in the pipeline, indicating that the oil flow rate gives an indication for determining the need for maintenance. The flow rate was categorized into three efficiency levels: High Efficiency (flow rate > 90% of the allowable rate), Moderate Efficiency (flow rate between 70% and 90%), and Low Efficiency (flow rate < 70%). Each efficiency level was linked to specific maintenance intervals: Specifically, a higher flow rate allowed longer intervals between maintenance activities, while a lower flow rate could indicate there is an accumulation of deposits that necessitates urgent intervention. Several machine learning models were trained, and variations in performance were observed. Gradient Boosting and XGBoost Regressor show the best performers with lower values for MSE, RMSE, and MAE and higher R² scores compared to the Support Vector Regressor. The result shows Gradient Boosting has an MSE of 0.000005, RMSE of 0.002259, MAE of 0.000968, and an R² of 0.997259, followed by XGBoost Regressor with MSE of 0.000005, an RMSE of 0.002269, an MAE of 0.000922, and an R² of 0.997234. Support Vector Regressor indicates the least performance, with an 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.