Next in Energy

Backed by a $3.5 million US Department of Energy grant, Texas A&M researchers are developing an AI-enabled drilling system that can identify critical minerals in real time, reducing exploration time and costs for rare earth element deposits.

Operational drift in oil and gas starts with small, undocumented deviations that compound over time, making real-time visibility, digital workflows, and frontline execution data critical for preventing safety incidents, compliance failures, and costly operational disruptions.

Students from around the globe are invited to participate in the 2026 Switch Global Case Competition to tackle the pressing issue of energy poverty and compete for $50,000 in prize money.

Discover how AI and machine learning are transforming oil and gas field development by reducing subsurface uncertainty, optimizing development decisions, and maximizing long-term reservoir value from concept selection through production.

As upstream operators move beyond isolated experiments, the hardest part of the AI journey is not building a model, it is making it stick.

Rodriguez reflects on engineering growth, systems thinking, and how strong technical foundations remain valuable across evolving industries.

How 7 decades of AI research, including the emergence of agentic AI, are reshaping oil, gas, renewables, and the grid.

The global energy transition is accelerating, but geopolitical tensions, infrastructure constraints, and behavioral barriers are increasingly shaping its pace, priorities, and long-term impact.

Methane abatement is emerging as a critical strategy for decarbonizing oil and gas operations, enhancing energy security, and creating commercial value by capturing emissions, meeting growing regulatory requirements, and supplying certified low-emissions natural gas to global markets.

A data-driven look at fuel savings, battery degradation, and net CO2 impact over a 10-year ownership period.