Knowledge acquisition and representation for intelligent operation support in offshore fields

Jing Wu, Morten Lind, Xinxin Zhang, Karnati Pardhasaradhi, Sharat Kumah Pathi, Claus Marner Myllerup

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    Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems' instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.
    Original languageEnglish
    JournalProcess Safety and Environmental Protection
    Pages (from-to)415-443
    Publication statusPublished - 2021


    • Knowledge acquisition
    • Knowledge representation
    • Intelligent decision support
    • Offshore fields
    • Functional modelling


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