Demonstrating good practice in the safe operation of gas assets with predictive analytics
Hennie Engelbrecht A C and Nesa Abbaspour B CA Woodside, Woodside Plaza, 240 St Georges Terrace, Perth, WA 6000, Australia.
B Accenture, Woodside Plaza, 240 St Georges Terrace, Perth, WA 6000, Australia.
C Corresponding authors. Email: hennie.engelbrecht@woodside.com.au; nesa.abbaspour@accenture.com
The APPEA Journal 57(2) 437-439 https://doi.org/10.1071/AJ16213
Accepted: 14 March 2017 Published: 29 May 2017
Abstract
The oil and gas industry operates large and complex facilities. Technical integrity (and thus licence to operate) must be maintained through routine inspection and maintenance regimes. Corrosion attacks every component at every stage in the life of every oil and gas field or plant (Schlumberger 1994). Globally, corrosion management accounts for US2.5Tr cross-industry spend (NACE International 2016).
Risk-based approaches for internal corrosion based on susceptibility of a process item to corrode, have been utilised to assist with identifying appropriate and more cost-effective maintenance and inspection strategies. The aim of such approaches is to protect integrity and not compromise safety; however, they do nothing to minimise regret cost. These approaches use only known physical characteristics of piping equipment and rely on repeat inspection data to calculate corrosion rates and associated maintenance schedules.
The present paper will leverage the challenges and shortcomings of using existing risk-based inspection (RBI) approaches and demonstrate how Accenture in collaboration with Woodside and others is utilising predictive analytics to more accurately determine likelihood of corrosion to exist in a more granular resolution, thus managing likelihood and consequence of corrosion to produce an improved risk-based model. The analytics model considers physical, geospatial and external factors for external corrosion.
This is a work in progress, with very promising initial results, that leads into the implementation of an improved RBI strategy, enabling Woodside to reduce inspection scope, physical site activity and associated management cost. In addition, it better manages plant risk in conjunction with smart visualisation tools.
Keywords: analytics, analytics model, corrosion, cost, inspection, LNG, machine learning, maintenance, planning, predictive, risk, risk-based inspection.
Hennie Engelbrecht is an engineering and project-management professional with a career spanning over 34 years. Hennie’s experience spans across the military, nuclear, mining, infrastructure environments, and oil and gas industries. His leadership roles have included research, operations-, projects-, facilities- and project services-management roles. Hennie joined Woodside in 2012 and worked on the Browse project with the Subsea and Pipelines project team. He joined the Karratha Life Extension (KLE) project in November 2013 where he was responsible for the External Corrosion project delivery in the Brownfields project team. In 2015, Hennie was tasked with continuous improvement and, among other things, predictive analytics within KLE. |
Nesa Abbaspour is a Technology Consultant in Accenture’s Perth, Australia, office. Nesa has experience in the Oil and Gas industry across the value chain. She has worked with major companies in the Upstream, Downstream and LNG segments, including two of the largest Australia’s Major Capital LNG Projects. Nesa focuses on helping Oil and Gas companies improve performance, reduce cost and increase efficiency and safety, through Data Analytics and using Digital Technologies. Nesa began her career with Accenture as a Business and Technology Integration Analyst. She has a Master of Financial Mathematics from University of Western Australia, and has BS in Applied Mathematics. |
References
Cavassi, P., and Cornago, M. (1999). The cost of corrosion in the oil and gas industry. Journal of Protective Coatings and Linings May, 30–40.Curtin (2009). Media release. Research shows corrosion costs the local economy. Available at http://news.curtin.edu.au/media-releases/research-shows-corrosion-costs-the-local-economy/ [Verified 17 March 2017].
NACE International (2016). ‘International Measures of Prevention, Application, and Economics of Corrosion Technologies Study.’ (NACE: Houston, TX.) Available at http://impact.nace.org/documents/Nace-International-Report.pdf [Verified 17 March 2017].
Schlumberger (1994). ‘Oilfield Review. Corrosion in the Oil Industry.’ Available at www.slb.com/~/media/Files/resources/oilfield_review/ors94/0494/composite.pdf [Verified 17 March 2017].
