Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines
Over recent years, a lot of money have been spent by the oil and gas industry to maintain pipeline integrity, specifically in handling CO2 internal corrosion. In fact, current solutions in pipeline corrosion maintenance are extremely costly to the companies. The empirical solutions also lack intelli...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Springer Verlag
2019
|
| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053592524&doi=10.1007%2f978-3-030-00211-4_22&partnerID=40&md5=356c85cd6b927bbba123bfb3299c8ca5 http://eprints.utp.edu.my/23530/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.utp.eprints.23530 |
|---|---|
| record_format |
eprints |
| spelling |
my.utp.eprints.235302021-08-19T07:57:34Z Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines Mohammad Zubir, W.M.A. Abdul Aziz, I. Jaafar, J. Over recent years, a lot of money have been spent by the oil and gas industry to maintain pipeline integrity, specifically in handling CO2 internal corrosion. In fact, current solutions in pipeline corrosion maintenance are extremely costly to the companies. The empirical solutions also lack intelligence in adapting to different environment. In the absence of a suitable algorithm, the time taken to determine the corrosion occurrence is lengthy as a lot of testing is needed to choose the right solution. If the corrosion failed to be determined at an early stage, the pipes will burst leading to high catastrophe for the company in terms of costs and environmental effect. This creates a demand of utilizing machine learning in predicting corrosion occurrence. This paper discusses on the evaluation of machine learning algorithms in predicting CO2 internal corrosion rate. It is because there are still gaps on study on evaluating suitable machine learning algorithms for corrosion prediction. The selected algorithms for this paper are Artificial Neural Network, Support Vector Machine and Random Forest. As there is limited data available for corrosion studies, a synthetic data was generated. The synthetic dataset was generated via random Gaussian function and incorporated de Waard-Milliams model, an empirical determination model for CO2 internal corrosion. Based on the experiment conducted, Artificial Neural Network shows a more robust result in comparison to the other algorithms. © Springer Nature Switzerland AG. 2019. Springer Verlag 2019 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053592524&doi=10.1007%2f978-3-030-00211-4_22&partnerID=40&md5=356c85cd6b927bbba123bfb3299c8ca5 Mohammad Zubir, W.M.A. and Abdul Aziz, I. and Jaafar, J. (2019) Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines. Advances in Intelligent Systems and Computing, 859 . pp. 236-254. http://eprints.utp.edu.my/23530/ |
| institution |
Universiti Teknologi Petronas |
| building |
UTP Resource Centre |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Petronas |
| content_source |
UTP Institutional Repository |
| url_provider |
http://eprints.utp.edu.my/ |
| description |
Over recent years, a lot of money have been spent by the oil and gas industry to maintain pipeline integrity, specifically in handling CO2 internal corrosion. In fact, current solutions in pipeline corrosion maintenance are extremely costly to the companies. The empirical solutions also lack intelligence in adapting to different environment. In the absence of a suitable algorithm, the time taken to determine the corrosion occurrence is lengthy as a lot of testing is needed to choose the right solution. If the corrosion failed to be determined at an early stage, the pipes will burst leading to high catastrophe for the company in terms of costs and environmental effect. This creates a demand of utilizing machine learning in predicting corrosion occurrence. This paper discusses on the evaluation of machine learning algorithms in predicting CO2 internal corrosion rate. It is because there are still gaps on study on evaluating suitable machine learning algorithms for corrosion prediction. The selected algorithms for this paper are Artificial Neural Network, Support Vector Machine and Random Forest. As there is limited data available for corrosion studies, a synthetic data was generated. The synthetic dataset was generated via random Gaussian function and incorporated de Waard-Milliams model, an empirical determination model for CO2 internal corrosion. Based on the experiment conducted, Artificial Neural Network shows a more robust result in comparison to the other algorithms. © Springer Nature Switzerland AG. 2019. |
| format |
Article |
| author |
Mohammad Zubir, W.M.A. Abdul Aziz, I. Jaafar, J. |
| spellingShingle |
Mohammad Zubir, W.M.A. Abdul Aziz, I. Jaafar, J. Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines |
| author_facet |
Mohammad Zubir, W.M.A. Abdul Aziz, I. Jaafar, J. |
| author_sort |
Mohammad Zubir, W.M.A. |
| title |
Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines |
| title_short |
Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines |
| title_full |
Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines |
| title_fullStr |
Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines |
| title_full_unstemmed |
Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines |
| title_sort |
evaluation of machine learning algorithms in predicting co2 internal corrosion in oil and gas pipelines |
| publisher |
Springer Verlag |
| publishDate |
2019 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053592524&doi=10.1007%2f978-3-030-00211-4_22&partnerID=40&md5=356c85cd6b927bbba123bfb3299c8ca5 http://eprints.utp.edu.my/23530/ |
| _version_ |
1738656485105205248 |
| score |
13.211869 |