Technical Topics

This commentary by the chair of the SPE Data Science and Engineering Analytics Technical Section examines how AI is reshaping petroleum engineering careers, highlighting growing risks to entry‑level training, judgment development, and the future pipeline of subject-matter experts in high‑consequence industries.

Lean Six Sigma, through its DMAIC framework, offers a data‑driven approach for reducing waste and variation in oil and gas operations and is explored here as a practical solution for improving drill-bit inventory and lease management despite limited industry adoption.

Gas chromatography is a proven tool supporting the safe, compliant, and optimal operation of operation of carbon capture, utilization, and storage processes.

The third and final part of the series covers the facility engineering and petroleum economics aspects of a field development plan.

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.