0

Full Content is available to subscribers

Subscribe/Learn More  >

Learning From Riser Analyses and Predicting Results With Artificial Neural Networks

[+] Author Affiliations
Kristian Authén

4Subsea, Hvalstad, Norway

Paper No. OMAE2017-61775, pp. V03BT02A056; 4 pages
doi:10.1115/OMAE2017-61775
From:
  • ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 3B: Structures, Safety and Reliability
  • Trondheim, Norway, June 25–30, 2017
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5766-3
  • Copyright © 2017 by ASME

abstract

Subsea wellheads are subjected to fatigue loading from semi-submersible drilling vessels in harsh and relatively shallow waters like in the North Sea. Dynamic finite element riser simulations are run to ensure safe operations. These simulations calculate loads and fatigue damage on the subsea wellheads, and are run in large numbers, often with only small alterations in input. Building and running all these models for each new well are both time consuming and costly.

By storing and structuring the results from such analyses, machine learning algorithms can be trained, and used to predict new results, without the need of running simulations for every new well. If there are insufficient simulation data available, data with inconsistent modeling, or little variety in the input of the data, it is also possible to use model builders to generate a sparse but sufficient set of simulation data to train the model.

A trained model can predict simulation results instantly, and gains accuracy as more analysis data becomes available for training.

The trained model is efficient for estimating fatigue status on oil fields with large numbers of subsea wells, since it is unnecessary with separate simulations for each well. It can also be used for early phase concept studies and as a QA tool for verifying results of other simulations.

Copyright © 2017 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

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 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