DSDE: In Practice

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.

The complete paper discusses the successful application of a data-driven approach to analyze production data and identify root causes of slugging in a subsea production system on the Norwegian Continental Shelf.

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.

The Northern Lights project will disclose data sets from the confirmation Well 31/5-7 Eos drilled in the North Sea and completed earlier this year.