Technical Topics

Agentic AI could help upstream oil and gas operations reduce emissions by enabling real-time methane detection, optimizing flaring and energy use, and improving carbon capture efficiency.

This article examines how domain experts can use no-code ML platforms to explore decision-relevant problems, validate hypotheses, quickly build prototypes, and engage more effectively with data science teams when solutions transition toward production.

Geoscientists are shifting from primarily discovering and extracting resources to integrating knowledge, guiding sustainable decisions, and using Earth’s history to help balance resource development with long-term planetary health.

Venezuela’s oil recovery will depend on restoring disciplined, reliable day-to-day operations by stabilizing existing assets, fixing operational failures, and using practical tools to rebuild predictable production.

Hydrogen is emerging as a key low-carbon energy carrier for the energy transition, with multiple production pathways that differ in cost, emissions, and scalability trade-offs.

Over the past decade, oilfield service companies have transformed logging-while-drilling (LWD) development into a faster, collaborative, system-level process that delivers improved reliability from the first run and makes development philosophy as important as the technology itself.

Digital transformation in oil and gas depends less on adopting advanced technologies and more on maturing data so people and processes can reliably convert raw information into aligned, asset-level value.

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.