From HAZOP Automation to Intelligent Real-Time Operator Decision Support by Means of Multilevel Flow Modelling

Abstract

Abstract For decades, efforts have been made to automate the HAZOP process. The motivation has mainly been to displace expensive manual HAZOP approaches, that are furthermore known to suffer from systemic quality issues related to system complexity, uncertainty, vagueness and level of knowledge completeness. With offset in a review of the main historic arguments for automating the HAZOP analysis, and an outline of the particular benefits of employing Multilevel Flow Modelling (MFM) theory in this context, this paper emphasises the opportunity to redeploy the insights achieved by the HAZOP team to assist an operator facing an abnormal event years later. By means of a detailed analysis of an actual catastrophic failure of a FPSO compression module, the paper demonstrates how MFM enabled HAZOP captures explicitly tacit expert knowledge about the complex interdependencies between process design, equipment design, safety barriers and instrumentation. The paper further describes a methodology to interpret measurements online by means of the MFM analysis, thereby establishing real-time cause and consequence analysis in sufficient time to interrupt the escalation from a benign sensor malfunction to a topside explosion. The paper concludes by outlining a knowledge management framework centred in MFM of the technical, operational and organizational safety barriers, which would make hitherto tacit knowledge explicitly available at all critical decision points during the lifecycle of a process plant, from design and HAZOP to commissioning, operation and decommissioning, as well as in any plant modification required along the way. The MFM theory was adapted and extended to capture the experienced failure mode and thereby facilitate HAZOP automation and subsequent intelligent real-time operator decision support.