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A data-driven decision support tool for offshore oil and gas decommissioning.

Vuttipittayamongkol, Pattaramon; Tung, Aaron; Elyan, Eyad

Authors

Pattaramon Vuttipittayamongkol

Aaron Tung



Abstract

A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders’ concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.

Citation

VUTTIPITTAYAMONGKOL, P., TUNG, A. and ELYAN, E. [2021]. A data-driven decision support tool for offshore oil and gas decommissioning. IEEE access [online], Early Access. Available from: https://doi.org/10.1109/ACCESS.2017.DOI

Journal Article Type Article
Acceptance Date Oct 1, 2021
Online Publication Date Oct 4, 2021
Deposit Date Oct 7, 2021
Publicly Available Date Oct 7, 2021
Journal IEEE Access
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/access.2021.3117891
Keywords Classification; Decision support tool; Machine learning; Offshore infrastructure; Oil and gas decommissioning; Supervised learning
Public URL https://rgu-repository.worktribe.com/output/1482008

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