Reliability, Availability and Maintainability (RAM) modelling study for a Liquid Treatment Facility (LTF) upgrade as part of FEED — determining the optimal Vapour Recovery Unit (VRU) compressor configuration to maximise production availability and minimise income loss from venting and flaring constraints.
Due to tightening environmental and government regulations, venting of combustible gases and the use of flaring had been reduced to a zero target. The associated off-gas from crude oil treatment processing could now only be vented or flared in emergency situations. As part of the FEED for a Liquid Treatment Facility upgrade, a Vapour Recovery Unit (VRU) was to be introduced to capture and boost this off-gas for re-injection into the AG manifold — making VRU equipment production critical. A loss of VRU compression would result in an immediate loss of oil production and oil flow. The operator needed to determine the optimal VRU compressor configuration before committing to detailed design.
Optimal conducted a Desktop Asset Verification (DAV) using P&IDs, equipment data, specifications, best industrial practices and operating philosophies. An AvSim Reliability Block Diagram (RBD) model was developed using Isograph software, representing failure and repair data for all equipment items with their inter-relationships and dependencies modelled according to statistical distributions. The model was run many times over using Monte Carlo simulation. Multiple compressor configurations and operating scenarios were evaluated to establish the optimal set-up with respect to availability, production capacity versus stoppages and annual income loss.
The study enabled the operator to select the optimal VRU compressor configuration on a quantified, evidence-based foundation — balancing availability, production capacity and income loss risk. The overall LTF RAM model provided a reusable asset that can be applied during operational phases to identify bad actors, optimise critical spares, prioritise maintenance and quantify the benefits of reducing logistic delay times.