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Electric-powered fracturing fleets looked impressively resilient in 2020 amid what was otherwise a significant collapse for the hydraulic fracturing sector. Today, the situation is more complicated.

Do you know how many of your organization’s wells are artificially lifted? Or, more importantly, do you know what fraction of your production volumes are dependent on artificial lift? I would wager that the percentages are higher than you would expect, and I encourage you to seek out that information and share it.

The paper demonstrates the application of an interpretable machine-learning work flow using surface drilling data to identify fracable, brittle, and productive rock intervals along horizontal laterals in the Marcellus shale.

The downtime of manufacturing machinery, engines, or industrial equipment can cause an immediate loss of revenue. Reliable prediction of such failures using multivariate sensor data can prevent or minimize the downtime. With the availability of real-time sensor data, machine-learning and deep-learning algorithms can learn the normal behavior of the sensor systems, dis…

A review of the properties of iron compounds and their impact in shale produced water treatment with an emphasis on their colloidal forms. A wide range of problems is associated with these compounds in produced water including emulsion stabilization, oil-coated solids, pad formation in separators, pipeline solids, and plugging of water disposal formations.

Few industries contain as many phases, steps, and levels of interface between the start and end product as the oil and gas industry. It therefore is hardly surprising that communication, in all its varied forms, is at the very heart of our business.

The backers of some of the first offshore hydrogen projects have little hope of turning a profit. The big question is, will anyone?

This paper describes the findings of a root-cause analysis of wellhead-penetrator failures in Canadian steam-assisted gravity-drainage operations and the mitigation measures taken.

The authors describe how tools in machine learning are used to develop data-driven models that can be used for accurate and efficient fatigue-damage prediction for marine risers.

The authors describe an integrated geological-engineering data-management project covering all aspects of well-engineering work flows.