RETRACTED ARTICLE: A comprehensive study on artificial intelligence in oil and gas sector

Gupta, Devansh; Shah, Manan

doi:10.1007/s11356-021-15379-z

Cited by 24 publications

(12 citation statements)

References 57 publications

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“…From the environmental perspective, it is one of the greater benefits of using AI that it offers a friendly environment to the workers and also integrates the different elements of the construction products to devise a strategy in which the waste is reduced along with safety risk factors that promote environmental degradation. The oil and gas industry is filled with many disasters that have negatively impacted the environment every time they occur (Gupta and Shah 2022 ).…”

Section: Research Article Citation Trendmentioning

confidence: 99%

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

et al. 2023

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Oil and gas construction projects are critical for meeting global demand for fossil fuels, but they also present unique risks and challenges that require innovative construction approaches. Artificial Intelligence (AI) has emerged as a promising technology for tackling these challenges, and this study examines its applications for sustainable development in the oil and gas industry. Using a systematic literature review (SLR), this research evaluates research trends from 2011 to 2022. It provides a detailed analysis of how AI suits oil and gas construction. A total of 115 research articles were reviewed to identify original contributions, and the findings indicate a positive trend in AI research related to oil and gas construction projects, especially after 2016. The originality of this study lies in its comprehensive analysis of the latest research on AI applications in the oil and gas industry and its contribution to developing recommendations for improving the sustainability of oil and gas projects. This research’s originality is in providing insight into the most promising AI applications and methodologies that can help drive sustainable development in the oil and gas industry.

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Section: Research Article Citation Trendmentioning

confidence: 99%

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

et al. 2023

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“…In the paper of Gupta et al [33], different types of machine learning applied to the oil and gas industry in upstream, midstream, and downstream processes are presented. Examples of how machine learning can optimize exploration, drilling, production, transportation, and refining operations are discussed.…”

Section: Keyword # Of Papers # Of Relevant Papersmentioning

confidence: 99%

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

2023

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Anomaly detection plays a crucial role in preserving industrial plant health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In complex systems, such as oil and gas, many components need to be kept operational. Predicting which parts will break down in a time interval or identifying which ones are working under abnormal conditions can significantly increase their reliability. Moreover, it underlines how the use of artificial intelligence is also emerging in the process industry and not only in manufacturing. In particular, the state-of-the-art analysis reveals a growing interest in the subject and that most identified algorithms are based on neural network approaches in their various forms. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas: (i) steady-state area, (ii) water anomaly macro-area, (iii) air-water anomaly area, (iv) tank anomaly area, (v) air anomaly macro-area, (vi) and steady-state transition area. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system. The proposed algorithm was tested on an experimental plant at Università Politecnica delle Marche.

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“…Even if the challenges towards a data-driven refinery operation can be high, the benefits of exploiting data from a holistic view can widely outweigh these costs. Guota et al [ 90 ] present a general study of Artificial Intelligence in the oil & gas sector, including upstream, midstream and downstream. The main downstream applications identified by these authors are related to expense reduction, production increase and improvements in maintenance and security.…”

Section: Potential Of Data Intelligencementioning

confidence: 99%

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Olaizola

Quartulli

Unzueta

et al. 2022

Sensors

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Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the “fourth industrial revolution” that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes.

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RETRACTED ARTICLE: A comprehensive study on artificial intelligence in oil and gas sector

Cited by 24 publications

References 57 publications

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

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