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A 3-year study that demonstrated how to capture a worker’s safety performance and translate the data into personal fatigue levels is the first step in creating a framework that can identify research-supported interventions that protect workers from injuries caused by being tired on the job.

Companies waste countless resources measuring the wrong things, not measuring at all, or failing to keep "the most important thing, the most important thing."

As part of the Papua New Guinea Liquefied Natural Gas project, a systematic longitudinal effort was made to collect a broad range of morbidity and mortality data for those communities directly adjacent to the project. These data were used to inform workplace disease-monitoring efforts.

Superior, Colorado, trustees approved a 6-month drilling moratorium ahead of plans to explore the town's oil and gas regulatory options.

Blockchain technology may have gotten its start by keeping cryptocurrency traders honest, but its usefulness is expanding. And the oil and gas industry is taking advantage.

Many leaders of petroleum engineering schools from around the world have never met but SPE’s technical director for academia would like to change that.

To test your knowledge, answer these 10 questions from Jim Crompton, who teaches petroleum data analytics at the Colorado School of Mines.

Digital solutions that have made their mark in other industries may foster stronger collaborative environments in various sectors within energy, including equipment maintenance and data management.

Oil companies are talking big about using data and analytics, but the experts in the field are not sure what their role will be. Birol Dindoruk, SPE's technical director for management and information, talks about ensuring that they have a say.

The backing of one of the world’s largest oil and gas companies aims to put this emerging digital technology on the path to widespread adoption.

2019’s class of offshore projects show a wide range of potential sanctioning outcomes.

A new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms.