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Managing hidden system threats for higher production regularity using intelligent technological solutions: a case study

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  • Jawad Raza
  • Jayantha Prasanna Liyanage

Abstract

The identification and interpretation of hidden system threats on complex Oil and Gas (O&G) production platforms has always been a challenge. These threats may gradually develop into failures/faults resulting in system shutdowns or, eventually, loss/reduction of production. The O&G industry is willing to test new technologies in managing uninterrupted, higher production regularity. In response to these challenges, a research project was initiated involving a leading oil company in Norway. A systematic investigative approach was adopted which incorporates domain experts' opinion and multiple information resources/databases. The paper attempts neural network modelling of a critical production loss-related scenario based on real plant data from an offshore production facility. Analytical results captured symptoms of suboptimal performance from compressors installed in the gas compression system. This methodology could give plant operators an opportunity to identify system's anomalies early. As a result, unwanted shutdowns can be avoided, consequently improving the overall plant's efficiency and productivity. [Received 30 October 2008; Revised 26 January 2009; Revised 21 May 2009; Accepted 13 June 2009]

Suggested Citation

  • Jawad Raza & Jayantha Prasanna Liyanage, 2010. "Managing hidden system threats for higher production regularity using intelligent technological solutions: a case study," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 4(2), pages 249-263.
    • Handle: RePEc:ids:eujine:v:4:y:2010:i:2:p:249-263
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    Cited by:

    1. Payman Eslami & Kihyo Jung & Daewon Lee & Amir Tjolleng, 2017. "Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(3), pages 538-550, August.

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