Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review
The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are produc-tion�based industries, is driven by enabling technologies which include machine learning (ML) and big...
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2022
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my.utp.eprints.335762022-09-07T08:27:00Z Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review Danyaro, K.U. Hussain, H.H. Abdullahi, M. Liew, M.S. Shawn, L.E. Abubakar, M.Y. The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are produc-tion�based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in�depth integration of Metocean data interoperability for intelligent operations and automation using an ML�based ap-proach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. MDPI 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131892080&doi=10.3390%2fapp12115690&partnerID=40&md5=b3382909198464e9ad2f88d583c1dc4b Danyaro, K.U. and Hussain, H.H. and Abdullahi, M. and Liew, M.S. and Shawn, L.E. and Abubakar, M.Y. (2022) Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review. Applied Sciences (Switzerland), 12 (11). http://eprints.utp.edu.my/33576/ |
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The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are produc-tion�based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in�depth integration of Metocean data interoperability for intelligent operations and automation using an ML�based ap-proach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| format |
Article |
| author |
Danyaro, K.U. Hussain, H.H. Abdullahi, M. Liew, M.S. Shawn, L.E. Abubakar, M.Y. |
| spellingShingle |
Danyaro, K.U. Hussain, H.H. Abdullahi, M. Liew, M.S. Shawn, L.E. Abubakar, M.Y. Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review |
| author_facet |
Danyaro, K.U. Hussain, H.H. Abdullahi, M. Liew, M.S. Shawn, L.E. Abubakar, M.Y. |
| author_sort |
Danyaro, K.U. |
| title |
Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review |
| title_short |
Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review |
| title_full |
Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review |
| title_fullStr |
Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review |
| title_full_unstemmed |
Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review |
| title_sort |
development and integration of metocean data interoperability for intelligent operations and automation using machine learning: a review |
| publisher |
MDPI |
| publishDate |
2022 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131892080&doi=10.3390%2fapp12115690&partnerID=40&md5=b3382909198464e9ad2f88d583c1dc4b http://eprints.utp.edu.my/33576/ |
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1744356187555495936 |
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