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.

石油和天然气行业（OGI）一直伴随着挑战和复杂性。它涉及许多流程和涉众，每个流程和涉众都生成大量数据。由于业务的全球性和分布式性质，处理和管理此信息是一项艰巨的任务。许多问题，例如协调不同的数据源，所有者和格式；在数据流沿着复杂的业务流程管道移动时对其进行验证，验证和保护；从数据中获取见解，以提高业务效率，安排维护时间并防止盗窃和欺诈。近年来，人工智能（AI）尤其是机器学习（ML）在包括OGI在内的许多领域都得到了广泛的接受，以帮助人类解决这些复杂的任务。此外，作为分布式AI子领域的多代理系统（MAS）满足了分布式系统的要求，并已在各种学科中得到成功的利用。多项研究探索了使用ML和MAS来提高运营效率，管理供应链并解决OGI中与生产和维护相关的各种任务。但是，机器学习仅应用于孤立的任务，尽管MAS在模拟环境中具有良好的性能，但它们尚未在石油和天然气公司中获得预期的普及。为了实现ML和MAS的潜力并鼓励它们在OGI中得到更广泛的接受，有必要在该领域进行进一步的研究。特别是，将ML嵌入到MAS中可以为行业的未来发展带来很多好处。