An Ontology for Offshore Oil & Gas Decommissioning

Adam Alexander Arfaoui, Alireza Maheri, Ana Ivanovic, Wamberto Vasconcelos

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


Planning offshore oil & gas decommissioning requires decision making based on expert domain knowledge. As more tools are developed to assist planners in simulating operations, there is an increased need for data models that can accurately represent the domain. The application of semantic web technologies has recently been suggested as a possible method to achieve this. The following paper outlines the creation of an Offshore Decommissioning Oil & Gas Ontology (ODOGO) using the OWL 2 web ontology language. This initially proposed version focuses on representing offshore site data and acts as a precursor to the development of a decommissioning decision support tool. An explanation of the keyword analysis, data modelling and visualisation steps that were employed to produce the schema is given. Use of ODOGO is illustrated using a case study of a decommissioning project set in the North Sea. Results of this study confirm the capabilities of using semantic data models to manage data for the decommissioning domain.
Original languageEnglish
Title of host publicationInternational Conference on the Decommissioning of Offshore & Subsea Structures DECOM 2020
Subtitle of host publicationProceedings
Number of pages14
Publication statusPublished - 1 Jan 2021
Event2nd International Conference on the Decommissioning of Offshore & Subsea Structures: DECOM 2020 - Aberdeen, United Kingdom
Duration: 30 Mar 202031 Mar 2020


Conference2nd International Conference on the Decommissioning of Offshore & Subsea Structures
Abbreviated titleDECOM 2020
Country/TerritoryUnited Kingdom
Internet address

Bibliographical note

This research is supported by the Oil and Gas Technology Centre (OGTC) in partnership with the National Decommissioning Centre.


  • Decommissioning
  • Ontology
  • Data Management
  • Offshore Oil & Gas
  • Semantic Web

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