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Engineering Poster E11: Using unsupervised machine learning to identify risk of failure at ageing oil and gas assets

Uday Manchanda A *
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A Kent, Perth, WA, Australia.

* Correspondence to: uday.manchanda@kentplc.com

The APPEA Journal 62 - https://doi.org/10.1071/AJ21423
Published: 3 June 2022

Abstract

Poster E11

Asset integrity management of ageing oil and gas assets is an ongoing challenge. This paper uses unsupervised algorithms (i.e. clustering technique) to identify carbon steel piping with increased probability of failure due to various internal corrosion mechanisms. The application used over 20 variables including wellhead planktonic bacterial counts, Fe2+ levels, oil and water production rates, historical Non-Destructive Testing (NDT) records, remaining life of downstream equipment, previous remediation data and geographical location data. An unsupervised machine learning clustering algorithm was written grounded in mathematical techniques of Principal Component Analysis (PCA) and k-means clustering. The probabilistic algorithm identified implicit patterns, which were then used to identify critical and non-critical piping clusters. Outputs from the clustering model were used to prioritise field measurements, and while these are ongoing there appears to be a good agreement with model predictions. The paper further discusses the measures that have a higher impact on the classification accuracy of the algorithm.

To access the poster click the link on the right. To read the full paper click here

Keywords: asset integrity, carbon steel piping, clustering, corrosion, internal corrosion mechanism, k-means, machine learning, oil and gas, principal component analysis, probability of failure, unsupervised machine learning.

Uday Manchanda is a technical safety engineering professional who holds a Bachelor of Science, Master of Professional Engineering and a Master of Commerce degrees from The University of Western Australia. His experience includes risk assessments, asset integrity, technical safety, flow-induced and acoustic vibrations, and continuous improvement. He was awarded a technical design prize by Yara for designing the best demineralisation system at an ammonia plant and the 2020 Perron Prize. He has a keen interest in advanced probabilistic risk assessments techniques.