data analytics

Supervised learning has many commercial applications; however, such learning lacks the capability to generate new insights and knowledge. In contrast, unsupervised learning discovers the inherent structures in unlabeled data, thereby helping generate new insights and actionable knowledge from large volumes of data.

Schneider Electric University has been designed to help data center professionals expand their skills by offering free guidance on the latest technology, sustainability, and energy efficiency initiatives.

Chief digital and information officer Sandeep Gupta's innovative use of technology has enabled the company to cut costs, reduce time to first oil, and manage decline in production.

The suite of tools includes a digital platform for sharing emissions data, an agreed set of definitions that nails down what different terms mean, a single set of metadata definitions, and an API through which users can access data. When used together, these tools provide an integrated perspective on what emissions are coming from where.

An operator has redefined onsite operations reporting through the development of a standardized set of reporting activity codes as the backbone of a standardized digital well-design and execution process.

This paper presents a screenout-classification system based on Gaussian hidden Markov models that predicts screenouts and provides early warning.

The two companies have teamed up in an attempt to cut downhole costs with a project that aims to extract more information from reduced data-acquisition programs.

SPE’s 2021 Open Subsurface workshop tackled the ins and outs of open source, open data, and open access.

The awards will be presented on 23 March at the Kuala Lumpur Convention Centre.

This article examines how clusters with different properties are produced by different clustering algorithms. In particular, it provides an overview of three clustering methods: k-Means clustering, hierarchical clustering, and DBSCAN.