Concurrent 18. Presentation for: Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin
Hossein Khalilpasha A *A Advisian Pty Ltd, Brisbane, Qld, Australia.
The APPEA Journal 62 - https://doi.org/10.1071/AJ21363
Published: 3 June 2022
Abstract
Presented on Wednesday 18 May: Session 18
The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan.
To access the presentation click the link on the right. To read the full paper click here
Keywords: artificial intelligence, corrosion, cost, in-line inspection, machine learning, pipeline integrity, remaining life.
Hossein Khalilpasha Leads Worley’s Asset Integrity Team at East Coast of Australia. He received his PhD from the University of Queensland where he investigated buckling integrity of pipelines. He has over 20 years of experience and an extensive knowledge in asset integrity management and governance. During his time working in the energy sector, Hossein has been involved in projects at different stages of asset lifecycle from feasibility study, design and build through to operation and maintenance. Hossein is Advisian’s a Subject Matter Expert in remaining life assessment and life extension studies being involved in numerous local and international projects. Hossein is active in multiple global research projects including contributing to the PRCI project on Pipeline Mid-wall Defect Detection and FFS among others. Hossein has been heavily involved in decarbonisation activities with multiple clients in Australia and globally at Worley, specifically with Hydrogen in pipeline. This has resulted in being involved in Future Fuel CRC research projects as industry advisor for multiple projects and also as committee member for the Integrity Work Group. He is also leading a research project for assessing the integrity and fitness for service of H2 pipelines. Hossein is one of the Authors of Australian ‘Code of Practice for H2 Pipelines’ which is currently under development. |
