Publications | Indonesian Petroleum Association

Revolutionizing Failure Detection with Machine Learning

Year: 2025 Authors: Komala, D., R., Lazuardi, H., Tanjungsari, F. Reference: IPA25-E-394

Keywords: Downtime reduction, Electric Submersible Pumps (ESP), Failure Prediction, Historical Data, Machine Learning, Predictive Analytics, Random Forest, Reliability, Sensor Data, oil production,

Basins:

Proceedings Title : Proc. Indon. Petrol. Assoc., 49th Ann. Conv., 2025

A failed Electrical Submersible Pump (ESP) well is typically identified when there is suboptimal flow or no flow reaching the surface. The process of...

A Machine Learning Approach for Early Fault Detection and Failure Prediction in Gas Turbines with Case Study: Avon 200 Combustor Crack and SOLAR Taurus 60-T7802 Thrust Balance Damage

Year: 2025 Authors: Fernanda, E., Arief, E., P., Afianto, D., Dadan, S. Reference: IPA25-F-395

Keywords: Anomaly detection, Condition-Based Monitoring, Failure Prediction, Gas Turbines, LSTM Auto Encoder, Machine Learning, Predictive modeling, Reliability, deep learning, preventive maintenance,

Basins:

Proceedings Title : Proc. Indon. Petrol. Assoc., 49th Ann. Conv., 2025

The critical in our oil and gas company as a failure can led to significant production and financial losses are gas turbines. Based on our operational...

Result for 2 item(s)

Revolutionizing Failure Detection with Machine Learning

A Machine Learning Approach for Early Fault Detection and Failure Prediction in Gas Turbines with Case Study: Avon 200 Combustor Crack and SOLAR Taurus 60-T7802 Thrust Balance Damage