Technical Topics

Agentic AI can enhance subsurface workflows when its autonomy is deliberately designed around physics, data integrity, and accountable decision-making through architectures that separate reasoning, computation, interpretation, and validation.

In complex energy projects, technical excellence alone is not enough—successful delivery depends on disciplined execution skills such as scope clarity, realistic scheduling, stakeholder coordination, and proactive risk management, particularly for young professionals turning concepts into real-world results.

The energy sector is rapidly transforming toward a data-driven, decentralized future where combining human expertise with AI and machine learning unlocks new efficiencies, solves complex challenges, and creates a decisive competitive advantage.

Marie-Hélène Pelletier presents a proactive framework for building resilience, managing uncertainty, and maintaining performance as AI reshapes work and life.

AI is transforming oil and gas, but the real change will come from young professionals (YPs) who bridge technology and field expertise. By leading pilots, building networks, and challenging old assumptions, YPs can drive the industry’s digital transformation from within.

By integrating AI into every layer of the energy ecosystem, from renewable forecasting to dynamic pricing, the path toward secure, sustainable, and affordable energy becomes not just possible but achievable.

Steel and cement are indispensable to modern society and the global energy transition, yet their production remains heavily dependent on fossil fuels—making them major contributors to greenhouse-gas emissions and posing a critical challenge to achieving full decarbonization by 2050.

This article introduces a tender strategy framework—a four-pillar model built on nearly 2 decades of international upstream experience—that integrates engineering rigor with business strategy to improve contract awards, strengthen transparency, and deliver lasting project performance.

Researchers at Colorado School of Mines explore an internally deployed fiber-optic distributed acoustic sensing (DAS) method for continuous gas-pipeline monitoring, demonstrating its potential to improve leak detection, enable early intervention, and enhance overall pipeline-integrity management compared to conventional inspection techniques.

Part 2 of this two-part series focuses on the disciplines of drilling engineering and production engineering.