Gaurav Verma
Gaurav Verma (Research Manager, IDC Energy Insights)

Oil and gas industry players have a mixed view of generative AI (GenAI). While the technology vendor community is so excited, oil and gas end-user organisations are cautious and are taking a more conservative position — for now. Maybe it’s still too early for them to commit or to comment on their next moves in the GenAI space.

This is reflected in IDC’s Future Enterprise Resilience and Spending Survey Wave 2 (March 2023), which shows that only 18% of oil and gas companies worldwide will invest in GenAI technologies this year. The remaining 82% are either neutral or are carrying out initial assessments to identify the best use cases.


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Potential Use Cases for the Oil and Gas Industry

There are three main use cases where oil and gas industry early adopters will be able to generate value with GenAI:

  1. Asset operations: GenAI can create new data and content to enhance multiscenario authentic simulations and prediction capabilities of operational assets. It can enhance the capabilities of digital twins, predictive maintenance and asset-management-specific workflow automation.
  2. Upstream subsurface data analysis: GenAI can enhance images to create 3D models. It can also generate subsurface images using fewer seismic data scans, avoiding the need for repeated data acquisitions to fill the data gaps that are common in the upstream oil industry.
  3. Enterprise ChatGPT for business leaders: Oil and gas companies’ unstructured data is generally held by different personas in different locations. All this data can be operationalised to create instant access to the right information to support organisations’ leadership in business decisions. Large language models (LLMs), such as ChatGPT, can play a crucial role here as they can generate human-like text, respond to domain questions and be used in the form of chatbots and virtual assistants.


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There are lot of uncertainties around the adoption of GenAI, such as development of new regulatory frameworks and organisations’ data security. Also, with oil and gas companies seeking to improve their ESG performance and making a serious commitment to net-zero emissions, they are trying to adopt new technologies to operate their business efficiently but with minimum possible environmental impact. One operational concern is the sustainability credentials of GenAI technologies, as the technology could have a huge carbon footprint. The training of a single common natural-language processing AI model, for example, emits nearly five times the emissions of a single car during its lifetime.

With GenAI at the early stages of adoption, there are still questions about how it will support business outcomes. How industries such as oil and gas utilise it will depend on how effectively it supports and enhances performance, while mitigating the risks that come with adopting a new technology. For the oil and gas market it seems that in the short term it’s a case of watch this space.

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