ABSTRACT

This paper presents a Digital Twin concept aimed at assets in the oil & gas and wind industry, that provides an accurate estimate of the true fatigue life of these assets in order to unlock potential fatigue life and ultimately extend the life of assets. Data from a few strategically placed sensors is decomposed into modal parameters by means of Operational Modal Analysis (OMA). The modal parameters are expanded to a highresolution stress field solution (MDE) via a calibrated FE-model (Digital Twin) representing the considered asset. The concept offers a compelling and cost-effective method for offshore assets that are facing life time extension beyond what current methodologies can provide. The concept is currently being implemented on a platform in the UK continental shelf of a supermajor oil company.

INTRODUCTION

According to the UK Health and Safety Executive (2012), approximately 50% of the fixed oil platforms on the UK continental shelf have exceeded their original 25-year design life. Using data from the European Marine Observation and Data Network (EMODnet) and assuming an original 25-year design life, approximately 38% of the all European oil platforms have exceeded their original design life. Over the next decade, this number will increase to around 80%. This is illustrated in Figure 1.

Low oil prices have fueled interest in extending the life of existing brownfield operations beyond their original design life, instead of investing in new greenfield operations. The life of existing oil platforms has been extended well beyond the original with improved simulation methods, but it is becoming increasingly difficult to extend the life further using current simulation methods. This has started the Digital Twin revolution, where 1:1 digital representations of real-world assets are able to describe the exact behavior of these assets. Four governing parameters control the motion of any structure: mass, damping, stiffness and load. In numerical models, there are significant uncertainties for these parameters which often results in overly conservative prediction of fatigue life. Digital Twins, based on accurate physics-based models augmented by real-world sensor data, drastically minimizes these inherent uncertainties, leading to improved estimates of the true fatigue life. When establishing Digital Twins for offshore assets, the nature and variation of modelling uncertainties to be considered are quite widespread.

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