Oil and Gas 4.0 era: A systematic review and outlook

Lu, Hongfang; Guo, Lei; Azimi, Mohammadamin; Huang, Kun

doi:10.1016/j.compind.2019.06.007

Cited by 219 publications

(104 citation statements)

References 20 publications

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“…They can serve as reference information to make energy policy and energy plan. As future work, the IGGM model can be applied in more application fields such as gas and oil prediction [74,75]. Meanwhile, the nonlinear grey action quantity can be adopted to optimize the fractional grey prediction model in the future.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

Zhang

She

2019

Sustainability

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Energy consumption is an essential basis for formulating energy policy and programming, especially in the transition of energy consumption structure in a country. Correct prediction of energy consumption can provide effective reference data for decision-makers and planners to achieve sustainable energy development. Grey prediction method is one of the most effective approaches to handle the problem with a small amount of historical data. However, there is still room to improve the prediction performance and enlarge the application fields of the traditional grey model. Nonlinear grey action quantity can effectively improve the performance of the grey prediction model. Therefore, this paper proposes a novel incomplete gamma grey model (IGGM) with a nonlinear grey input over time. The grey input of the IGGM model is a revised incomplete gamma function of time in which the nonlinear coefficient determines the performance of the IGGM model. The WOA algorithm is employed to seek for the optimal incomplete coefficient of the IGGM model. Then, the validations of IGGM are performed on four real-world datasets, and the results exhibit that the IGGM model has more advantages than the other state-of-the-art grey models. Finally, the IGGM model is applied to forecast Japan’s solar energy consumption in the next three years.

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Section: Discussionmentioning

confidence: 99%

Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

Zhang

She

2019

Sustainability

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“…It is true that one of the key points implemented in industry 4.0 is how to reduce environmental pollution, as shown by Abdul Moktadir et al in a recent paper [9], but also to increase safety by receiving real time inputs about hotspots of the system; for example, in a high-risk industrial sector such as the petrochemical one, the industry 4.0 approach is used to monitor big data from a variety of sources, as highlighted by Hongfang Lu et al [10].…”

Section: Introductionmentioning

confidence: 99%

Modelling a Safety Management System Using System Dynamics at the Bhopal Incident

et al. 2020

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In a safety management system (SMS), risk management plays a key role for the prevention of accidents. This study aims to propose a safety management model by using a system dynamics approach applied to the Bhopal incident. The model proposed in this paper discusses the relationships among the main causes that have contributed to the occurrence of the incident studied, such as broken safety devices, inadequate personnel experience, operator decisions, manager production strategy, policy decision, as deduced from the relevant literature about the Bhopal incident dynamics. The simulation and the results clarify how to manage the key factors to make the plant safe. Moreover, we want to underline how these key modern aspects of safety could be enhanced by using the enabling technologies of industry 4.0.

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“…The oil and gas (O&G) organizations industrial automation infrastructure landscape is complex [7]. To perform focused and effective studies, Industrial systems infrastructure is divided into functional levels by The Instrumentation, Systems and Automation Society (ISA) Standard ANSI/ISA-95:2005 [8].…”

Section: Introductionmentioning

confidence: 99%

Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures

et al. 2020

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Oil and Gas organizations are dependent on their IT infrastructure, which is a small part of their industrial automation infrastructure, to function effectively. The oil and gas (O&G) organizations industrial automation infrastructure landscape is complex. To perform focused and effective studies, Industrial systems infrastructure is divided into functional levels by The Instrumentation, Systems and Automation Society (ISA) Standard ANSI/ISA-95:2005. This research focuses on the ISA-95:2005 level-4 IT infrastructure to address network anomaly detection problem for ensuring the security and reliability of Oil and Gas resource planning, process planning and operations management. Anomaly detectors try to recognize patterns of anomalous behaviors from network traffic and their performance is heavily dependent on extraction time and quality of network traffic features or representations used to train the detector. Creating efficient representations from large volumes of network traffic to develop anomaly detection models is a time and resource intensive task. In this study we propose, implement and evaluate use of Deep learning to learn effective Network data representations from raw network traffic to develop data driven anomaly detection systems. Proposed methodology provides an automated and cost effective replacement of feature extraction which is otherwise a time and resource intensive task for developing data driven anomaly detectors. The ISCX-2012 dataset is used to represent ISA-95 level-4 network traffic because the O&G network traffic at this level is not much different than normal internet traffic. We trained four representation learning models using popular deep neural network architectures to extract deep representations from ISCX 2012 traffic flows. A total of sixty anomaly detectors were trained by authors using twelve conventional Machine Learning algorithms to compare the performance of aforementioned deep representations with that of a human-engineered handcrafted network data representation. The comparisons were performed using well known model evaluation parameters. Results showed that deep representations are a promising feature in engineering replacement to develop anomaly detection models for IT infrastructure security. In our future research, we intend to investigate the effectiveness of deep representations, extracted using ISA-95:2005 Level 2-3 traffic comprising of SCADA systems, for anomaly detection in critical O&G systems.

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Oil and Gas 4.0 era: A systematic review and outlook

Cited by 219 publications

References 20 publications

Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

Modelling a Safety Management System Using System Dynamics at the Bhopal Incident

Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures

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