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The 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.
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Norwegian oil and gas industry (NOGI) Innovation Technology Digital transformation AI Machine learning (ML) Competitive advantage