DSDE: In Practice

Have our institutions become too complex to secure?

Recent advances could make feasible the deployment of networks of methane sensors to detect the greenhouse gas at large facilities.

At Equinor’s giant new North Sea oil field, thousands of sensors feed into Data Gumbo’s novel blockchain platform—encoding an immutable record of operations, the better to automate contracts, pay vendors, and (in the not too distant future) even measure carbon emissions.

The proposed solution is a good candidate for real-time burner-efficiency monitoring and automatic alarm triggering and optimization.

The authors develop an innovative machine-learning method to determine salt structures directly from gravity data.

The work and the provided methodology provide a significant improvement in facies classification.

This paper details the introduction of a drilling automation system to deliver superior well-construction performance in a major gas field.

The complete paper presents a methodology designed for optimally matching drill bits, mud motors, and bottomhole-assembly components for reduced failure risks and improved drilling performance.

A new study confirms the success of a natural-gas leak-detection tool pioneered by Los Alamos National Laboratory scientists that uses sensors and machine learning to locate leak points at oil and gas fields, promising new automatic, affordable sampling across a vast natural gas infrastructure.

Advances during the past decade in using convolutional neural networks for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent.