A study on the reliability of oil and gas blowout preventer (BOP) technologies under deep-water erratic conditions

E. M. Enjema, M. Shafiee, A. Kolios

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)

Abstract

Evaluating the reliability performance of the Blowout Preventer (BOP) technologies has gained increasing interest in the oil and gas industry after the Deepwater Horizon disaster in 2010. One important concept in BOP reliability is redundancy configuration of critical subsystems. Selecting the most suitable configuration for BOP is often based on pressure rating and general service conditions of the field. As drilling advances into deeper waters, the same selection procedure is followed, without considerable attention to erratic and dynamic conditions in these new environments. This paper proposes a Hybrid Dynamic Bayesian Network (HDBN) model incorporating erratic temperature and pressure data and corresponding effects on reliability, to support BOP choice in deep waters. Maximum operating limits are modelled for two different configurations by a two-parameter Weibull distribution and corresponding reliability function of each component is derived. Sensitivity of the model to some key input parameters is investigated, and finally, optimal decision is weighed alongside other factors such as cost, operability and weight footprint.

Original languageEnglish
Title of host publicationSafety and Reliability – Theory and Applications - Proceedings of the 27th European Safety and Reliability Conference, ESREL 2017
EditorsMarko Cepin, Radim Briš
Place of PublicationAbington
Pages2411-2416
Number of pages6
Edition1st
DOIs
Publication statusPublished - 25 May 2017
Event27th European Safety and Reliability Conference, ESREL 2017 - Portorož, Slovenia
Duration: 18 Jun 201722 Jun 2017

Conference

Conference27th European Safety and Reliability Conference, ESREL 2017
Country/TerritorySlovenia
CityPortorož
Period18/06/1722/06/17

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