Technical Topics

For upstream operations, blockchain can help in delivering trusted and secure data, faster transactions, and increased collaboration across complex data chains.

AI is beginning to transform well management by helping engineers predict electrical submersible pump failures before they happen, optimize drawdown more efficiently, and generate reliable forecasts even when data is scarce or noisy.

The oil and gas industry's shift to smart fields—driven by automation, AI, and real-time data—requires petroleum engineers to master digital technologies alongside traditional skills.

This article explores how early-career engineers can build the competencies needed to support sustainability in oil and gas operations, not just for efficiency but also for climate resilience.

Part 1 of this series focuses on the disciplines of geology and geophysics, petrophysics, and reservoir engineering using real-world field examples from Malaysia and the author's experiences in training undergraduate students in Malaysian universities.

Part 2 in this series in hydrofracturing is intended to give early-career petroleum engineers a comprehensive and accessible look into modern fracturing practices.

This article explores the integration of hydrogen into existing natural gas infrastructure and introduces practical solutions, including the application of machine learning models, to support decision-making and infrastructure adaptation in the energy transition.

Effective management of produced water in mature oil fields is essential to reducing environmental risks and operational costs, with subsurface disposal emerging as the most efficient and responsible method.

This article examines the safety of geological carbon storage by addressing common concerns, presenting evidence-based assessments, and exploring the actual measures in place to ensure its secure and responsible deployment. 

Mineralogical, mechanical, and flow complexities in major US shale plays are tightly linked, making traditional 1D modeling inadequate. Emmanuel Obasi, SPE, addresses this with a physics-informed ML approach detailed in this article.