The global energy industry is experiencing a profound transformation. In the quest for greater efficiency, sustainability, and resilience, companies are increasingly turning to machine learning (ML) as a strategic differentiator.
Energy companies generate massive volumes of data—from production sensors and petrophysical logs to analytics. Traditional approaches struggle with the scale and complexity of the available data. Machine learning, however, excels at pattern detection, predictive modeling, and real-time decision-making.
The most innovative energy companies are deploying machine learning in impactful ways:
| Company | ML Application Highlights |
|---|---|
| BP | Uses AI to steer drilling operations and analyze seismic data—reducing well planning time from months to weeks and improving drilling efficiency. |
| Devon Energy | Improves well productivity by 25% using ML models across drilling assets. |
| Chevron | Utilizes AI-powered drones for remote equipment monitoring, reducing downtime and improving continuity. |
| Shell | Employs over 100 AI applications—from predictive maintenance to materials forecasting—and monitors approximately 10,000 assets. |
| Aramco | Operates an internal AI Hub using ML in sustainability, asset health, and quality control. |
| Volue (Norway) | Develops ML-powered trading and water-management tech, aiding in renewable transition and reducing resource waste. |
| Shell, RWE, BP, AGL Energy (Australia) | Shell: 15% efficiency gain in AI-enhanced drilling. RWE: reduced grid faults by 20%. BP: extended field lifecycles via ML analytics. AGL: improved farm generation efficiency by 25% and personalized consumer energy services. |