A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0

Nguyen, Trung; Gosine, Raymond G.; Warrian, Peter

doi:10.1109/access.2020.2979678

Cited by 105 publications

(37 citation statements)

References 103 publications

(113 reference statements)

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“…Since then, there have been numerous research and industrial implementations across many industrial sectors [38]. The scope of the literature review presented in this article, however, is limited to the O&G industry and follows a similar approach to that presented in [11], [38]- [42]. TABLE 1 and FIGURE 1 outline the article selection criteria for this study.…”

Section: Methodsmentioning

confidence: 99%

Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

et al. 2020

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With the emergence of industry 4.0, the oil and gas (O&G) industry is now considering a range of digital technologies to enhance productivity, efficiency, and safety of their operations while minimizing capital and operating costs, health and environment risks, and variability in the O&G project life cycles. The deployment of emerging technologies allows O&G companies to construct digital twins (DT) of their assets. Considering DT adoption, the O&G industry is still at an early stage with implementations limited to isolated and selective applications instead of industry-wide implementation, limiting the benefits from DT implementation. To gain the full potential of DT and related technological adoption, a comprehensive understanding of DT technology, the current status of O&G-related DT research activities, and the opportunities and challenges associated with the deployment of DT in the O&G industry are of paramount importance. In order to develop this understanding, this paper presents a literature review of DT within the context of the O&G industry. The paper follows a systematic approach to select articles for the literature review. First, a keywords-based publication search was performed on the scientific databases such as Elsevier, IEEE Xplore, OnePetro, Scopus, and Springer. The filtered articles were then analyzed using online text analytic software (Voyant Tools) followed by a manual review of the abstract, introduction and conclusion sections to select the most relevant articles for our study. These articles and the industrial publications cited by them were thoroughly reviewed to present a comprehensive overview of DT technology and to identify current research status, opportunities and challenges of DT deployment in the O&G industry. From this literature review, it was found that asset integrity monitoring, project planning, and life cycle management are the key application areas of digital twin in the O&G industry while cyber security, lack of standardization, and uncertainty in scope and focus are the key challenges of DT deployment in the O&G industry. When considering the geographical distribution for the DT related research in the O&G industry, the United States (US) is the leading country, followed by Norway,

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Section: Methodsmentioning

confidence: 99%

Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

et al. 2020

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“…Lately, the adoption of technology for this purpose has been widely discussed, such as the implementation of 3D printing of spare parts for local supply to avoid transportation; and the adoption of Blockchain, Internet of Things (IoT), and digital twins for supply chain transparency [19,65,66]. For vessel and deck space optimization, the adoption of big data analytics and machine learning/artificial intelligence has been widely discussed [67][68][69].…”

Section: Discussionmentioning

confidence: 99%

Cleaning Up Our Act: Systems Engineering to Promote Business Model Innovation for the Offshore Exploration and Production Supply Chain Operations

Czachorowski

2021

Sustainability

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Oil and gas offshore exploration and production (E & P) will remain necessary to meet increasing global energy demands. However, appraising and exploring these resources has a major impact on sustainability and faces many challenges. Improving the supply chain operations that support E&P activities presents opportunities to contribute to the United Nations (UN) Sustainable Development Goals (SDGs), but relies on organizations being able to adopt new strategies and technology and, innovate their current business models. Business model innovation (BMI) has not been actively pursued in this industry, partially due to the traditional operation management and due to the complexity in changing established models or adopting full-fledged archetypes. Thus, the present study proposes a more flexible and granular approach to BMI by defining elements to be adopted rather than proposing business models archetypes. To define the elements, an application of systems engineering (SE) is adopted through a morphological analysis (MA). They are presented in morphological boxes in three dimensions—technology, organization, and the human element—inspired by sustainable business model (SBM) literature. The elements are proposed as “bricks” for BMI where they can be adopted and re-arranged as necessary, providing granularity and flexibility to facilitate BMI for organizations of varying sizes.

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“…The basic assumption of the model is that each pharmaceutical company implements the Industry 4.0 strategy in the production of drugs and uses one or many of the different concepts of horizontal communication, i.e., Cloud Computing [ 44 ], IIoT [ 45 ], Connectivity [ 46 ], Big Data [ 47 ], Digital Twins [ 48 ], ID/Loc [ 46 , 49 ], Semantics [ 50 , 51 ], SOA [ 52 ]. Of course, we can assume other solutions, but the eight mentioned are key concepts of the CPS I4.0.…”

Section: Methodsmentioning

confidence: 99%

Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

Topolski

2021

Sensors

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The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.

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A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0

Cited by 105 publications

References 103 publications

Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

Cleaning Up Our Act: Systems Engineering to Promote Business Model Innovation for the Offshore Exploration and Production Supply Chain Operations

Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

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