DSDE: In Practice

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.

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.

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.

Automated image-processing algorithms can improve the quality and speed in classifying the morphology of heterogeneous carbonate rock. Several commercial products have produced petrophysical properties from 2D images and, to a lesser extent, from 3D images.

The complete paper describes an automated machine-learning approach to determine the spatial variation in decline type curves for shale gas production, based on existing data of production, completion, and geological parameters.

The complete paper describes the development of the “digital field worker” at Johan Sverdrup, an initiative that has changed the approach toward not only construction and completion but also operations.

The complete paper discusses optimization of a development plan involving low-salinity water injection.