FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2979741026> ?p ?o ?g. }

• W2979741026 endingPage "100191" @default.
• W2979741026 startingPage "100191" @default.
• W2979741026 abstract "The oil and gas industry (OGI) has always been associated with challenges and complexities. It involves many processes and stakeholders, each generating a huge amount of data. Due to the global and distributed nature of the business, processing and managing this information is an arduous task. Many issues such as orchestrating different data sources, owners and formats; verifying, validating and securing data streams as they move along the complex business process pipeline; and getting insights from data for improving business efficiency, scheduling maintenance and preventing theft and fraud are to be addressed. Artificial intelligence (AI), and machine learning (ML) in particular, have gained huge acceptance in many areas recently, including the OGI, to help humans tackle such complex tasks. Furthermore, multi-agent systems (MAS) as a sub-field of distributed AI meet the requirement of distributed systems and have been utilised successfully in a vast variety of disciplines. Several studies have explored the use of ML and MAS to increase operational efficiency, manage supply chain and solve various production- and maintenance-related tasks in the OGI. However, ML has only been applied to isolated tasks, and while MAS have yielded good performance in simulated environments, they have not gained the expected popularity among oil and gas companies yet. Further research in the fields is necessary to realise the potential of ML and MAS and encourage their wider acceptance in the OGI. In particular, embedding ML into MAS can bring many benefits for the future development of the industry. This paper aims to summarise the efforts to date of applying ML and MAS to OGI tasks, identify possible reasons for their low and slow uptake and suggest ways to ensure a greater adoption of these technologies in the OGI." @default.
• W2979741026 created "2019-10-18" @default.
• W2979741026 creator A5011092623 @default.
• W2979741026 creator A5031619356 @default.
• W2979741026 date "2019-11-01" @default.
• W2979741026 modified "2023-10-16" @default.
• W2979741026 title "Machine learning and multi-agent systems in oil and gas industry applications: A survey" @default.
• W2979741026 cites W1772172785 @default.
• W2979741026 cites W1995961111 @default.
• W2979741026 cites W2004874932 @default.
• W2979741026 cites W2014301059 @default.
• W2979741026 cites W2022487574 @default.
• W2979741026 cites W2026536622 @default.
• W2979741026 cites W2031508581 @default.
• W2979741026 cites W2052642746 @default.
• W2979741026 cites W2085037356 @default.
• W2979741026 cites W2107544712 @default.
• W2979741026 cites W2131241853 @default.
• W2979741026 cites W2131882595 @default.
• W2979741026 cites W2146482048 @default.
• W2979741026 cites W2147492008 @default.
• W2979741026 cites W2165808003 @default.
• W2979741026 cites W2166075343 @default.
• W2979741026 cites W2514033443 @default.
• W2979741026 cites W2563247128 @default.
• W2979741026 cites W2570275096 @default.
• W2979741026 cites W2769325239 @default.
• W2979741026 cites W2780908698 @default.
• W2979741026 cites W2896338148 @default.
• W2979741026 cites W3216720137 @default.
• W2979741026 doi "https://doi.org/10.1016/j.cosrev.2019.08.002" @default.
• W2979741026 hasPublicationYear "2019" @default.
• W2979741026 type Work @default.
• W2979741026 sameAs 2979741026 @default.
• W2979741026 citedByCount "59" @default.
• W2979741026 countsByYear W29797410262019 @default.
• W2979741026 countsByYear W29797410262020 @default.
• W2979741026 countsByYear W29797410262021 @default.
• W2979741026 countsByYear W29797410262022 @default.
• W2979741026 countsByYear W29797410262023 @default.
• W2979741026 crossrefType "journal-article" @default.
• W2979741026 hasAuthorship W2979741026A5011092623 @default.
• W2979741026 hasAuthorship W2979741026A5031619356 @default.
• W2979741026 hasBestOaLocation W29797410262 @default.
• W2979741026 hasConcept C108713360 @default.
• W2979741026 hasConcept C111919701 @default.
• W2979741026 hasConcept C136197465 @default.
• W2979741026 hasConcept C144133560 @default.
• W2979741026 hasConcept C151730666 @default.
• W2979741026 hasConcept C154945302 @default.
• W2979741026 hasConcept C15744967 @default.
• W2979741026 hasConcept C162324750 @default.
• W2979741026 hasConcept C162853370 @default.
• W2979741026 hasConcept C174998907 @default.
• W2979741026 hasConcept C199360897 @default.
• W2979741026 hasConcept C202444582 @default.
• W2979741026 hasConcept C206729178 @default.
• W2979741026 hasConcept C21547014 @default.
• W2979741026 hasConcept C2522767166 @default.
• W2979741026 hasConcept C2780586970 @default.
• W2979741026 hasConcept C33923547 @default.
• W2979741026 hasConcept C41008148 @default.
• W2979741026 hasConcept C43521106 @default.
• W2979741026 hasConcept C526740375 @default.
• W2979741026 hasConcept C77805123 @default.
• W2979741026 hasConcept C85345410 @default.
• W2979741026 hasConcept C86803240 @default.
• W2979741026 hasConcept C9652623 @default.
• W2979741026 hasConcept C98045186 @default.
• W2979741026 hasConceptScore W2979741026C108713360 @default.
• W2979741026 hasConceptScore W2979741026C111919701 @default.
• W2979741026 hasConceptScore W2979741026C136197465 @default.
• W2979741026 hasConceptScore W2979741026C144133560 @default.
• W2979741026 hasConceptScore W2979741026C151730666 @default.
• W2979741026 hasConceptScore W2979741026C154945302 @default.
• W2979741026 hasConceptScore W2979741026C15744967 @default.
• W2979741026 hasConceptScore W2979741026C162324750 @default.
• W2979741026 hasConceptScore W2979741026C162853370 @default.
• W2979741026 hasConceptScore W2979741026C174998907 @default.
• W2979741026 hasConceptScore W2979741026C199360897 @default.
• W2979741026 hasConceptScore W2979741026C202444582 @default.
• W2979741026 hasConceptScore W2979741026C206729178 @default.
• W2979741026 hasConceptScore W2979741026C21547014 @default.
• W2979741026 hasConceptScore W2979741026C2522767166 @default.
• W2979741026 hasConceptScore W2979741026C2780586970 @default.
• W2979741026 hasConceptScore W2979741026C33923547 @default.
• W2979741026 hasConceptScore W2979741026C41008148 @default.
• W2979741026 hasConceptScore W2979741026C43521106 @default.
• W2979741026 hasConceptScore W2979741026C526740375 @default.
• W2979741026 hasConceptScore W2979741026C77805123 @default.
• W2979741026 hasConceptScore W2979741026C85345410 @default.
• W2979741026 hasConceptScore W2979741026C86803240 @default.
• W2979741026 hasConceptScore W2979741026C9652623 @default.
• W2979741026 hasConceptScore W2979741026C98045186 @default.
• W2979741026 hasFunder F4320335413 @default.
• W2979741026 hasLocation W29797410261 @default.
• W2979741026 hasLocation W29797410262 @default.
• W2979741026 hasOpenAccess W2979741026 @default.

• next