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

doi:10.1016/j.psep.2021.09.036

Abstract

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.

Highlights

Good operators’ performance (Rasmussen, 1983) in the process industry is one of the key elements to keep the plant working in the normal operating range and preventing production losses
This paper provides case studies that illustrate the effectiveness of intelligent operation support by applying Multilevel Flow Modelling (MFM) to an off-shore water injection system
The principle and method of an interface linking between instrumentations and measurements and functions in MFM models have been introduced

Summary

Introduction

Good operators’ performance (Rasmussen, 1983) in the process industry is one of the key elements to keep the plant working in the normal operating range and preventing production losses. Process systems are socio-technical systems supporting operators’ performance can be improved from two aspects: human and systems. Re searches focus on offline operators’ training with simulators and human factor analysis (Ahmad et al, 2016; Dalijono et al, 2005; Omidi et al, 2018). These studies aim to improve the human cog nitive capability for situation awareness (Endsley et al, 2003; Gordon, 1992; Naderpour et al, 2015).

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Discussion

Conclusion