Application of Monte Carlo simulation in offshore hydrocarbon QRA modelling

Studenteropgave: Kandidatspeciale og HD afgangsprojekt

  • Mathias Rohwer Bang Nielsen
4. semester, Sikkerhed og Risikostyring (, Kandidat (Kandidatuddannelse)
This project presents a probabilistic framework in the non-dedicated software VBA/Excel for strengthening the Danish industry standard in the modelling of offshore hydrocarbon risks in Quantitative Risk Assessments. The study utilizes Monte Carlo simulation and computational crowd simulation to accommodate the future trends within risk analysis in an industry, where quantification of risk is legally required.
A generic and representative offshore installation setup is developed as a test environment, including complete 3D drawings, process streams, process sections and a representative manning setup. A primary muster scenario is created in the computational crowd simulation software Pathfinder in order to calculate the time step resolution for the probabilistic hydrocarbon model.
The inverse transform method is used to pick samples from selected continuous distribution functions in the Monte Carlo algorithm and a special user-friendly Monte Carlo interface is developed. Additionally, quantitative verification and validation methods document the model behavior by testing the algorithm using a one step analysis, extreme condition tests and a statistical goodness of fit test.
Case studies test the model capabilities and are discussed in relation to the selected software platform, hydrocarbon consequence modelling, post processing techniques, computational crowd simulation, decision-making, conservatism and uncertainty.
The results support the potential of this project’s model at this stage of development and presents, how the use of Monte Carlo simulation and computational crowd simulation is able to increase the experts’ confidence in the results by improved post processing compared to the Danish industry standard.

Udgivelsesdato9 jan. 2018
Antal sider134
Ekstern samarbejdspartnerRamboll Foundation
Carsten Stegelmann
ID: 267228636