Machine Learning and Multi-Agent Systems in Oil and Gas Industry Applications: A Survey

Hanga, Khadijah and Kovalchuk, Yevgeniya (2019) Machine Learning and Multi-Agent Systems in Oil and Gas Industry Applications: A Survey. Computer Science Review. ISSN 1574-0137 (In Press)

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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 subfield 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.

Item Type: Article
Date: 6 August 2019
Subjects: G700 Artificial Intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Mohamed Gaber
Date Deposited: 07 Aug 2019 06:18
Last Modified: 07 Aug 2019 06:18
URI: http://www.open-access.bcu.ac.uk/id/eprint/7838

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