Application of Monte Carlo simulation in offshore hydrocarbon QRA modelling
Author
Nielsen, Mathias Rohwer Bang
Term
4. term
Education
Publication year
2018
Submitted on
2018-01-09
Pages
134
Abstract
Dette projekt præsenterer en sandsynlighedsbaseret ramme i det ikke-specialiserede software VBA/Excel til at styrke den danske industristandard for modellering af offshore kulbrinterisici i kvantitative risikoanalyser. Metoden kombinerer Monte Carlo-simulering (mange gentagne, tilfældige kørsler for at fange variation) og beregningsmæssig crowd-simulering (modellering af, hvordan mennesker bevæger sig og samles under en hændelse) for at imødekomme fremtidige tendenser i en branche, hvor risici skal kvantificeres ved lov. Som testmiljø er der udviklet et generisk og repræsentativt offshore-anlæg med komplette 3D-tegninger, processtrømme, procesafsnit og et realistisk bemandingssetup. Et primært samlingsscenario (muster) er oprettet i crowd-simuleringssoftwaren Pathfinder for at fastlægge den passende tidssteg-opløsning i den probabilistiske kulbrintemodel. Monte Carlo-algoritmen anvender invers transform-metoden til at trække tilfældige værdier fra udvalgte kontinuerte fordelinger, og der er udviklet en brugervenlig grænseflade i Excel. Kvantitative verifikations- og valideringsmetoder dokumenterer modellens adfærd gennem one-step analyse, tests under ekstreme betingelser og en statistisk goodness-of-fit-test. Casestudier afprøver modellens kapabiliteter og diskuteres i forhold til den valgte softwareplatform, konsekvensmodellering for kulbrinter, efterbehandling af resultater, crowd-simulering, beslutningstagning, konservatisme og usikkerhed. Resultaterne peger på modellens potentiale på dette udviklingstrin og viser, at brugen af Monte Carlo og crowd-simulering, sammen med forbedret efterbehandling, kan øge eksperters tillid til resultaterne sammenlignet med den danske industristandard.
This project presents a probabilistic framework built in non-specialist software (VBA/Excel) to strengthen the Danish industry standard for modeling offshore hydrocarbon risks in Quantitative Risk Assessments. The approach combines Monte Carlo simulation (running many randomized iterations to capture variability) and computational crowd simulation (modeling how people move and assemble during an incident) to align with future trends in a sector where risk quantification is required by law. A generic, representative offshore installation was developed as a testbed, including complete 3D drawings, process streams, process sections, and a realistic staffing setup. A primary muster scenario was created in the crowd simulation software Pathfinder to determine a suitable time-step resolution for the probabilistic hydrocarbon model. The Monte Carlo algorithm uses the inverse transform method to sample from selected continuous distributions, and a user-friendly Excel interface was developed. Quantitative verification and validation document model behavior through a one-step analysis, extreme-condition tests, and a statistical goodness-of-fit test. Case studies assess the model’s capabilities and are discussed with respect to the chosen software platform, hydrocarbon consequence modeling, post-processing, crowd simulation, decision-making, conservatism, and uncertainty. The results indicate the model’s potential at this stage and show how Monte Carlo and crowd simulation, together with improved post-processing, can increase expert confidence compared with the current Danish industry standard.
[This abstract was generated with the help of AI]
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