Artificial intelligence could add $500 billion to oil and gas companies by 2030

Estimates from Rystad Energy suggest that digitalization and artificial intelligence technologies could generate nearly $500 billion in cumulative value for oil and gas exploration and production companies between 2026 and 2030.

This value is expected to be achieved through:

• Lower costs by improving operational efficiency

• Higher production through increased uptime and enhanced recovery rates

• Shorter project development timelines

Cost savings and production growth are expected to be the two largest sources of value through 2030, with both contributing at similar levels.

Exploration and production companies currently investing in digitalization and AI are expected to generate an additional $80 billion annually by 2030 compared to 2025 levels.

Results are already beginning to emerge across the sector.

ADNOC announced that AI-driven initiatives generated $500 million in value during 2023, while allocating $1.5 billion to digital spending with the goal of achieving $1 billion annually in added value.

Meanwhile, Equinor achieved nearly $200 million in AI-related savings between 2021 and 2024, before recording another $130 million during 2025 alone.

The report noted that digital value creation follows an accelerating cumulative curve as adoption expands and organizational capabilities mature within companies.

The estimated $500 billion opportunity is distributed across four main categories:

• Asset development

• Operations and maintenance

• Exploration and reservoir development

• Drilling, wells, and production

Digital maturity levels vary across these segments. Operations and maintenance currently show the fastest adoption pace, particularly through predictive maintenance and remote operations, which have reduced costs by double-digit percentages at some major companies.

Subsurface and reservoir-related activities are viewed as having the largest untapped potential, especially in boosting production volumes and lowering drilling costs. Some companies have already reduced seismic data interpretation times from several months to roughly 10 days.

The report also stated that artificial intelligence does not necessarily raise the performance ceiling for top-performing companies, but instead helps the broader industry move closer to the standards achieved by leading firms.

In the US shale sector, major producers are already approaching the physical limits of drilling efficiency. As a result, the greatest benefit now lies in improving average well performance. The study estimates potential improvements of around 10% on average across US onshore fields, while savings in some complex deepwater projects could exceed 50%, although a more realistic range is estimated between 15% and 20%.

This comes as exploration and production companies spent nearly $25 billion on AI tools and digital solutions last year. Forecasts suggest that the market for these services will grow by more than $10 billion by 2030, exceeding $35 billion annually before approaching $50 billion by 2035.

The report argues that the main obstacle to achieving these gains is not a lack of technology, but the difficulty of implementing it at scale. As a result, companies are increasingly forming partnerships with technology providers and oilfield service firms to reduce complexity and accelerate integration between different systems and equipment.

It also noted that most current AI applications in the oil industry rely on traditional machine learning models that require years of training data and are often difficult to transfer from one field to another without significant redevelopment.

However, newer technologies such as “agentic AI” — capable of performing tasks in a semi-autonomous manner — could accelerate digital transformation in the future by reducing gaps between departments and connecting different types of data without requiring full retraining.

Under an optimistic scenario, the annual value generated by digital initiatives could rise to $150 billion by 2030, with the potential to exceed $300 billion annually by 2035, compared to the base-case estimate of only $178 billion in 2035.

Achieving this scenario would also require increasing spending on digital solutions to $50 billion annually by 2030, before rising to nearly $80 billion by 2035.

The report concluded by noting that while artificial intelligence accelerates gains inside digitally mature organizations, it does not necessarily shorten the digital transformation journey itself.