Data Integration for Offshore Decommissioning Waste Management

Abiodun Akinyemi, Ming Sun, Alasdair J. G. Gray

Research output: Contribution to journalArticle

Abstract

Offshore decommissioning represents significant business opportunities for oil and gas service companies. However, for owners of offshore assets and regulators, it is a liability because of the associated costs. One way of mitigating decommissioning costs is through the sales and reuse of decommissioned items. To achieve this effectively, reliability assessment of decommissioned items is required. Such an assessment relies on data collected on the various items over the lifecycle of an engineering asset. Considering that offshore platforms have a design life of about 25 years and data management techniques and tools are constantly evolving, data captured about items to be decommissioned will be in varying forms. In addition, considering the many stakeholders involved with a facility over its lifecycle, information representation of the items will have variations. These challenges make data integration difficult. As a result, this research developed a data integration framework that makes use of Semantic Web technologies and ISO 15926 - a standard for process plant data integration - for rapid assessment of decommissioned items. The proposed solution helps in determining the reuse potential of decommissioned items, which can save on cost and benefit the environment.
Original languageEnglish
Article number103010
JournalAutomation in Construction
Volume109
Early online date13 Nov 2019
DOIs
Publication statusPublished - Jan 2020

Fingerprint

Data integration
Waste management
Costs
Semantic Web
Information management
Industry
Sales
Gases

Keywords

  • Data integration
  • ISO 15926
  • Offshore decommissioning
  • Reuse
  • Semantic Web

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

Cite this

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Data Integration for Offshore Decommissioning Waste Management. / Akinyemi, Abiodun; Sun, Ming; Gray, Alasdair J. G.

In: Automation in Construction, Vol. 109, 103010, 01.2020.

Research output: Contribution to journalArticle

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