0
research-article

LAZY-WAVE BUOYANCY LENGTH REDUTION BASED ON FATIGUE RELIABILITY ANALYSIS

[+] Author and Article Information
Vinicius Silva

Federal University of Rio de Janeiro/COPPE Rio de Janeiro, Brazil
vini@poli.ufrj.br

Luis V. S. Sagrilo

Federal University of Rio de Janeiro /COPPE Rio de Janeiro, Brazil
sagrilo@coc.ufrj.br

Mario Alfredo Vignoles

Federal University of Rio de Janeiro/COPPE Rio de Janeiro, Brazil
mavignoles@technip.com

1Corresponding author.

ASME doi:10.1115/1.4038937 History: Received June 15, 2017; Revised December 14, 2017

Abstract

The current downturn of the oil and gas industry force managers to take hard decisions about the continuity of projects, resulting in delays, postponements or even their cancellation. In order to keep with them, the rush for cost reduction is a reality and the industry is pushing the involved parties to be aligned with this objective. The Brazilian Pre-Salt region, characterized by ultra-deep waters, faces this scenario where flexible risers in lazy-wave configurations are usually adopted as a solution to safe transfer fluids from sea bed until the floating unit. The smaller the buoyancy length is the cheaper the project become, reducing the necessary amount of buoys and the time spent for its installation. This paper investigates the possibility of buoyancy length reduction of lazy-wave configurations by using structural reliability methods on fatigue failure mode. The application of the fatigue reliability approach considers four 6" flexible riser configurations: an original lazy-wave, a lazy-wave with less 30% of buoyancy length, another one with less 50% of buoyancy length and a free-hanging. Failure probabilities and safety factor calibration curves are shown for each configuration and compared among themselves. The results indicate the possibility of defining a lazy-wave configuration with smaller buoyancy lengths, reaching 75% of reduction without changing the preconized high safety class. Structural reliability analysis is available to help engineers understand the driving random variables of the problem, supporting the actual scenario of cost reduction for better decision-makings based on quantified risk.

Copyright (c) 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In