%R 10.2523/IPTC-21493-MS %D 2021 %T Machine Learning as Accelerating Tool in Remote Operation Realisation through Monitoring Oil and Gas Equipments and Identifying its Failure Mode %O cited By 1; Conference of 2021 International Petroleum Technology Conference, IPTC 2021 ; Conference Date: 23 March 2021 Through 1 April 2021; Conference Code:187135 %K Costs; Damage detection; Data Analytics; Data visualization; Failure (mechanical); Gas industry; Infill drilling; Maintenance; Petroleum prospecting; Predictive analytics; Support vector machines, Critical challenges; Damage costs; Environmental damage; Equipment failures; Gas equipment; Machine-learning; Oil and gas; Oil and Gas Industry; Plant equipments; Remote operation, Gasoline %I International Petroleum Technology Conference (IPTC) %L scholars15297 %A A.N.C.A. Naufal %A S.A.S.N. Samy %A N.H. Nenisurya %A Z.M. Zaharuddin %A E.D.D. Eddy %A A.A. Amir %A M.H.H. Hilmi %A I.A. Aziz Izzatdin %A J.J. Jafreezal %A N.A. Aziz Norshakirah %A E.A.P. Akhir Emelia %A K.A.A.K.A. Amirul %A H.M. Razip Hajar %A A.W.R. Wahyu %A N.S.M. Jaafar Syakirah %X Equipment failure, unplanned downtime operation, and environmental damage cost represent critical challenges in overall oil and gas business from well reservoir identification and drilling strategy to production and processing. Identifying and managing the risks around assets that could fail and cause redundant and expensive downtime are the core of plant reliability in oil and gas industry. In the current digital era; there is an essential need of innovative data-driven solutions to address these challenges, especially, monitoring and diagnosis of plant equipment operations, recognize equipment failure; avoid unplanned downtime; repair costs and potential environmental damage; maintaining reliable production, and identifying equipment failures. Machine learning-artificial intelligence application is being studied to develop predictive maintenance (PdM) models as innovative analytics solution based on real-data streaming to get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected. This paper proposes novel machine learning predictive models based on extreme learning/support vector machines (ELM-SVM) to predict the time to failure (TTF) and when a plant equipment(s) will fail; so maintenance can be planned well ahead of time to minimize disruption. Proper visualization with deep-insights (training and validation) processes of the available mountains of historian and real-time data are carried out. Comparative studies of ELM-SVM techniques versus the most common physical-statistical regression techniques using available rotating equipment-compressors and time-failure mode data. Results are presented and it is promising to show that the new machine learning (ELM-SVM) techniques outperforms physical-statistics techniques with reliable and high accurate predictions; which have a high impact on the future ROI of oil and gas industry. Copyright © 2021, International Petroleum Technology Conference. %J International Petroleum Technology Conference, IPTC 2021