DSDE: In Practice

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.

As companies begin to embrace the concepts of digitalization and big data, the main challenge remains: How do we make a step change in reducing human error in heavily paper-based operating and maintenance procedures?

Recent advances in data technology and machine learning have disrupted many businesses and processes and can lead to a new paradigm in workplace safety as well. This case study demonstrates the application of data science and predictive analytics to aid the health, safety, and environment function.