Probabilistic downtime estimation for sequential marine operations

Willem E.L. Bruijn*, Jolien Rip, Antoon J.H. Hendriks, Pieter H.A.J.M. van Gelder, Sebastiaan N. Jonkman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
111 Downloads (Pure)

Abstract

A marine project consists of series of operations, with each operation subject to a predefined operational limit and duration. If actual weather conditions exceed the operational limit, the operation cannot be executed and hence downtime occurs. An accurate assessment of uncertainties and the expected downtime during a marine project is important in the tender and execution phase. This paper proposes a new probabilistic model for downtime estimation. It utilizes linked Markov chains that use actual metocean conditions to produce binary workability sequences for each operation. Synthetic time-series can be generated based on the statistics of the past observations and more project simulations are realizable, reducing the simulation uncertainty. The capabilities and limitations of the proposed approach are illustrated in a case study for a hypothetical project in the Tasman Sea.
Original languageEnglish
Pages (from-to)257-267
Number of pages11
JournalApplied Ocean Research
Volume86
DOIs
Publication statusPublished - 2019

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Chains
  • Downtime
  • Linked
  • Marine project operations
  • Markov theory
  • Offshore
  • Probability
  • Sequences
  • Simulation
  • Synthetic
  • Time series
  • Workability

Fingerprint

Dive into the research topics of 'Probabilistic downtime estimation for sequential marine operations'. Together they form a unique fingerprint.

Cite this