Data & Analytics

This paper presents a mathematical analysis of how incorrect estimates of initial reservoir pressure may affect rate-transient analysis in ultralow-permeability reservoirs.

In this work, novel physics-based models and machine-learning models are presented and compared for estimating permanent-downhole-gauge measurements.

The paper describes a method to match reaction kinetics from coreflooding experiments.

Drillers are working to find ways to break some bad data habits. Those problems can range from the use of multiple formulas to calculate mechanical specific energy to timekeeping systems where the clocks and the time records are often off.

Connected safety is growing, including in the oil patch. And it is tied to the wearable technology so prevalent today.

The AIoT has the potential to transform industries and society, and it is already starting to have an impact. This article will explore the principles of AIoT, its benefits, and its current use.

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.

Researchers from Skoltech have trained a neural network to recognize rock samples in core box images efficiently. The process has sped up analysis by up to 20 times and made it possible to automate the description of rock samples.

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.