Abstract
Oil and gas refinery operations are under constant pressure to enhance efficiency and ensure uninterrupted processing. The adoption of predictive maintenance strategies has emerged as a pivotal solution, enabling real-time anomaly detection, predicting pressure fluctuations, and monitoring asset health. An illuminating example hails from a downstream operator in Western Australia that strategically harnesses the power of IoT and AI/ML. For them, revenue hinges on the streamlined delivery of gas processing services to customers, amplifying the significance of process efficiency gains. Leveraging on-site equipment data analysis, this approach significantly minimizes on-site maintenance requirements and automates back-office tasks, reducing manual data analysis and response generation in maintenance permit systems. The technical infrastructure involves wireless sensor-enabled data collection transmitted to a centralized hub, where machine learning algorithms detect equipment defects. Rapid reporting of these defects to decision-makers, accompanied by contextual insights, empowers swift, informed decision-making. This innovative solution has expanded business horizons, enabling the processing of gas for external entities alongside producing their reservoir gas in the downstream processing plant.
