DSDE: Features

A lot has been learned about shale, but those working to eke out oil from that ultratight rock still extract more value from data than physics.

Physics-based simulations plus machine-learning exercises are yielding a more comprehensive look at production volumes from unconventional assets.

Smaller and independent upstream companies often have limited resources for data management. Nonetheless, their data are valuable and must be managed for that value to be realized. Geologists may just be in the perfect position to do the job, if they can get the training.

New contracts, a joint venture, and a merger are pushing the company's robotic vessel fleet further into the mainstream of both hydrocarbons and renewable energy inspection services.

A look behind-the-scenes reveals what it takes to protect innovative ideas in the fast-moving US shale sector—key lessons learned, pitfalls to avoid, and how to do it the right way, as told by two of the operator's intellectual property (IP) protection leaders.

NASA is undertaking a drilling project on the Moon—the first on any planetary body outside the Earth. The mission’s purpose, collecting subsurface soil and ice samples to search for water beneath the lunar surface, is being compared to collecting core samples on Earth and will use technologies, such as a rotary-percussive drill, that have been adapted from those for h…

Anomalies in heart function can be diagnosed in real time by measuring an electrical signal. Petroleum engineers have adapted the concept to diagnose anomalous drilling conditions in real time using a shock signature recorded downhole.

Oslo-based consultancy Rystad Energy sees the potential for robots and automation to replace up to 400,000 roles in North America, Europe, and Russia by 2030. The timeline is not guaranteed, though, and will move at a speed set by human decision making.

Scientists at the Sandia National Laboratories are using machine learning to find methane hydrates on the seafloor, using an approach similar to weather forecasting.

Economical oil extraction depends largely on reducing wear and tear of downhole equipment and tubulars through regular monitoring and batch treatment to cut the risk of corrosion and costly workovers. Having a data-driven approach to scheduling batch treatment can bring down the frequency of treatment and avoid the unnecessary use of resources.