DSDE: Features

As the saying goes, the future is now. This is certainly the case offshore Norway, which represents one of the industry’s most influential test beds for impactful innovation.

At least a dozen satellites are being built to orbit the Earth in search of methane leaks. They will be measuring methane flows, and some will be able to identify the source down to the facility level.

Start-and-stop data management initiatives and a mishmash of partial solutions are no longer viable for managing the digital oil field. Data management should be transformed from a cost center to a cash-flow-generating function.

Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, and sports. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface en…

Wintershall Dea set out to demystify digital for engineers with an informal network of staff experts who help fill the gaps in this new way of doing things and have a focus on maximizing the return on problems previously solved.

Digital data startups face many hazards, from potential customers unwilling to share data to buyers who just do not see the payoff in what they are selling.

The oil industry’s rush to capitalize on digital change has scrambled relations with service providers big and small, giving the oil companies a greater say in the process.

Shell, C3 AI, Baker Hughes, and Microsoft have teamed up to launch the Open AI Energy Initiative platform that is designed to present AI-based reliability applications to improve operational efficiency.

The oil and gas industry, which had begun showing signs of recovery from a generational downturn, was brought to its knees by the COVID-19 pandemic in 2020. SPE’s technical directors reflect on the pandemic’s impacts and share their outlooks going forward in this annual roundup.

Innovators at the Norwegian oil company have developed a machine-learning model that analyzes mud-gas data to predict the gas/oil ratio of wells as they are drilled—something that the industry has worked for decades to accomplish.