Rajsingh, PeterBrekke, Josefine Smith2020-03-032020-03-032020-01-272020http://hdl.handle.net/10400.14/29806The downturn in the Norwegian oil industry in recent years has led to a revaluation of the sector. Out of this turmoil, a new surge of innovation appeared. This paper explores the innovation effects machine learning (ML) technology has brought to the Norwegian oil and gas industry (NOGI) using a qualitative approach through conducting semi-structured qualitative interviews. These interviews focus on five unique perspectives within the industry. These perspectives represent the unique interplay between private and public actors on the Norwegian continental shelf (NCS). The interviews discuss the value of big data, the use of ML in optimizing extraction processes and finding more sustainable approaches to detecting oil and gas. After presenting the five perspectives in the analysis, similarities and differences are discussed in light of the role the actors i.e. the companies play on the NCS. Interviewees expressed their enthusiasm and aversions about using new technologies to secure competitive advantages, despite most companies developing similar uses of ML. Throughout the analysis, background information from website searches and analyses are used to provide context for the interview data. The results show that the use of data, advanced analytics and various forms of ML create opportunities to fundamentally reimagine how and where work gets done and that there are possibilities of finding safer, more cost efficient and more sustainable approaches to the work currently being done through ML in the NOGI. The study shows that ML has brought disruptive innovation to the NOGI that enhances competitive advantages.engNorwegian oil and gas industry (NOGI)InnovationTechnologyDigital transformationAIMachine learning (ML)Competitive advantagemaster thesis202440010