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Tools, Technologies and Frameworks for Digital Twins in the Oil and Gas Industry: An In-Depth Analysis.
Meza, Edwin Benito Mitacc; Souza, Dalton Garcia Borges de; Copetti, Alessandro; Sobral, Ana Paula Barbosa; Silva, Guido Vaz; Tammela, Iara; Cardoso, Rodolfo.
Afiliação
  • Meza EBM; Institute of Science and Technology, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.
  • Souza DGB; Institute of Science and Technology, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.
  • Copetti A; Institute of Science and Technology, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.
  • Sobral APB; Institute of Science and Technology, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.
  • Silva GV; Institute of Science and Technology, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.
  • Tammela I; Institute of Science and Technology, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.
  • Cardoso R; Institute of Science and Technology, Fluminense Federal University, Rio das Ostras 28895-532, Brazil.
Sensors (Basel) ; 24(19)2024 Oct 06.
Article em En | MEDLINE | ID: mdl-39409497
ABSTRACT
The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça