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In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.

A Shell partnership with YPF marks a significant milestone for the Argentina LNG export facility, raising new questions about the nation’s potential to unlock the economic power of its vast shale reserves.

The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.

Everywhere you look these days, there is talk of how advances in big data, artificial intelligence, and machine learning will revolutionize virtually every aspect of our lives. This month, we will look at how researchers in the drilling domain are using this potential to improve well construction.

Updates about global exploration and production activities and developments.

Regional pore-pressure variations in the Leonardian- and Wolfcampian-age producing strata in the Midland and Delaware basins are studied using a variety of subsurface data.

As reservoir complexity and energy-transition demands grow, the industry is developing new logging technologies, integrating multidisciplinary functions, and conducting new experiments to address new challenges. This Technology Focus highlights three papers from conventional, unconventional, and carbon-storage projects.

The study uses laboratory and digital core analyses of Berea sandstone to estimate petrophysical and dynamic properties for adjustment of predicted precipitation and flow reduction in reservoir simulation models of intermittent CO₂ injection with aquifer drive.

Global offshore decommissioning projects hear the starting gun in Australia and the North Sea, but will the race be a marathon or a sprint?

Greenhouse gases are woven into every stage of a well’s life, which presents challenges that demand creative solutions that do not require too much capital.