Achieving Operational Efficiencies from a Centralized Alarm Management System

Alarm Management Systems ("AMS") have been adopted in the oil & gas industry where several benefits were realized. Such as improved panel operator effectiveness, maintaining higher levels of plant uptime and integrity, reducing the number of abnormal situation. Which ultimately leads to higher asset productivity. Several OPCO have multiple operational assets/sites that are geographically diverse. Where each asset might have a different Integrated Control System ("ICS") due to the time and availability of technology at the time of commissioning. Requiring diverse locally implemented AMS. A unified CAMS thus reduces time and effort to develop, deploy, and maintain alarm systems and is an essential toolkit for enhanced safe operation of the plant. Some sites have multiple plants wuth common pocess control section. The process control enginners needs to visit individual plants access DCS alalrms. By carryinhour corporate alarm management, engibbers at their office PCs have the access to the DCS alarms. Implementing CAMS requires the presence of a robust data presence infrastructure in place. Notably a centralized plant information management system, where several real time data points with regards to alarms and operator inputs can be captured. A CAMS unifies the approach of how alarm management is conducted in the company. Where a CAMS system generates a set of standard and custom templates that highlight the performance of each operating asset/shift/panel operator. Providing insights into the performance of each asset, efficiency of each operational shift and response of the panel operators. That when addressed, will lead to an overall performance and production of the operational asset. With this alarm management data, it can be further enhanced through data analytics to identify areas where operational efficiencies can be achieved. Additionally, the CAMS reduces the times and effort to deploy an alarm management system for any future operating asset expansions. CAMS coupled with real time data and Machine learning algorithms, past behaviours of the plant can be correlated, which can then be utilised for future predictions on alarms. This would further enhance our data driven decision-making, and would reduce the dependence on personal driven decisions. It can be concluded, that the CAMS is worthy investment for operating companies that have geographical/ICS diverse operational assets.