Data Driven Alarm Rationalization: Implementing Effective Alarm Systems in Oil Field Operations

As the oil and gas industries progressively relying on modern automated control systems to monitor, control and safeguard plants, alarms can be configured with very small effort and high volumes. Therefore, the number of alarms can become overwhelming for the operators dealing with more alarms than the usual. Subsequently, leading to alarm fatigue where important alarms can be ignored or missed. This increases the difficulty of responding to plants' upset which can lead to possible accidents or unplanned shutdowns. Alarm rationalization is the critical process of analyzing, evaluating, and refining alarm systems to ensure that alarms are meaningful and actionable. This process involves reducing the high number of redundant or false alarms, categorizing them based on their priority, and ensuring that each alarm provides clear and actionable information. The main goal is to minimize unnecessary or false alarms, improve the plant's safety and enhance operational reliability by having the operators focus on the critical alerts. In this paper, we explore how a data-driven approach was applied to improve alarm systems in a heavy oil field operation. By enforcing the "Alarm Documentation and Rationalization (D&R)" procedure, we developed a robust system that uses data analysis to create an action-driven alarm system. This system is designed to alert and guide operations to corrective actions without causing distraction, confusion, or conflict. We describe the methods, procedures, results, and conclusions of the project, showcasing how it significantly reduced unnecessary alarms and boosted operator efficiency.