AI strategies, asset optimization and data quality: the new frontier for oil and gas

Energy Transition Talks

AI strategies, asset optimization and data quality: the new frontier for oil and gas

In the latest episode of our Energy Transition Talks, Maida Zahid sits down with CGI experts Mark van Engelen and Curtis Nybo to discuss the growing role of artificial intelligence (AI) in the oil and gas space. Specifically, they look at the evolution of—and need for—generative AI in the industry, the value of an iterative, domain-based approach to implementation and cross-industry AI use cases to advance the energy transition.

The new frontier for AI in oil and gas: data, demographics and domain-based approaches

The use of AI to support the asset-heavy oil and gas industry has been in effect for some time, especially for optimizing asset maintenance and predictive maintenance. However, new areas of need are driving the evolving role and growing value of AI within organizations.
First, Mark mentions, is the need for generative AI to help unlock the vast amounts of data in the oil and gas companies (e.g., on the GIS side, on their land side, upstream, downstream, etc.). This rise of ‘data GTP’ as he calls it, means gaining access to that data in a natural language format to pose questions like, ‘How many barrels did you produce last month?’ without clicking through several layers of reporting.
Second, as shifting demographics and changing workforces expose a knowledge gap between retiring experts and new professional entrants, generative AI is helping organizations bridge the gap and provide access to legacy knowledge in an efficient manner.
More crucial than vast amounts of data is the quality of the data. When working on use cases with clients, Curtis says they begin with domains that have decent data quality or supporting data management processes, to maximize ROI and time to completion.
As he explains, “we take a domain-based approach, where in parallel as you’re working on an AI project in the one domain, you can clean up the data of another domain next on your list,” so you’re not applying AI to the whole company at once; you’re starting with one area or team and expanding throughout the organization.
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