Operationalising Consequence Analysis Via a Digital Twin For Offshore Compressor Trains

Natural gas compressor trains on offshore platforms present elevated fire and explosion risk due to methane's wide flammability and low ignition energy. This study models methane releases leading to dispersion and jet fires in a three-stage compressor train and embeds the outputs in a digital twin to enable predictive anomaly detection and decision support. Consequence modelling used DNV PHAST v8.7: the Unified Dispersion Model (UDM) for dispersion and the Johnson solid-flame model for jet-fire analysis. Three compressor discharge scenarios (low, medium, and high-pressure) were evaluated under two meteorological conditions ( 2  m  s⁻¹ / F and 5  m  s⁻¹ / D ) for a representative 30 mm leak on an anonymised North Sea platform. Under worst-case stability ( 2  m  s⁻¹ / F ), 100% LFL cloud extents reached 9.45  m, 16.75  m, and 30.55 m for the low, medium and high-pressure stages, respectively. Corresponding thermal-radiation footprints at 4  kW  m⁻² extended to 25.4  m, 46.31  m, and 78.95 m , with lethal 37.5 kWmm⁻² contours up to 52.59 m at high pressure; predicted flame lengths ranged 17.0−46.0  m. These outputs were integrated into the twin as software artefacts, hazard footprint overlays and triggerable scenario selection, supporting proactive anomaly detection, detector placement, exclusion zone definition, and emergency response planning. The work demonstrates how validated consequence models can be operationalised via a digital twin to provide live, spatially precise risk intelligence for offshore process safety.

Keywords: Digital Twin, Consequence Modelling, Methane Dispersion, Offshore Process Safety, Quantitative Risk Assessment.