Once in orbit, MethaneSAT's software and spectrometers, which measure different wavelengths of light to detect methane, will pinpoint both concentrated locations for methane plumes as well as the broader areas where the gases diffuse and spread. It will also use Google's image detection algorithms to create the first comprehensive, global map of the oil and gas industry's infrastructure, like pump jacks and storage tanks, where leaks most commonly occur. "Once those maps are lined up, we expect people will be able to have a far better understanding of the types of machinery that contribute most to methane leaks," says Yael Maguire, who leads geo-sustainability efforts at Google. This tool could solve a significant stumbling block for methane researchers, according to Rob Jackson, professor of Earth system science at Stanford. There are millions of oil and gas operations around the world, and information about where many of these facilities are located is tightly guarded, and where available, expensive to access.

In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous operating conditions. To alleviate their task, we present a system consisting of an autonomously navigating robot integrated with various sensors and intelligent data processing. It is able to detect methane leaks and estimate its flow rate, detect more general gas anomalies, recognize oil films, localize sound sources and detect failure cases, map the environment in 3D, and navigate autonomously, employing recognition and avoidance of dynamic obstacles. We evaluate our system at a wastewater facility in full working conditions. Our results demonstrate that the system is able to robustly navigate the plant and provide useful information about critical operating conditions.

Methane is one of the most potent greenhouse gases, and its short atmospheric half-life makes it a prime target to rapidly curb global warming. However, current methane emission monitoring techniques primarily rely on approximate emission factors or self-reporting, which have been shown to often dramatically underestimate emissions. Although initially designed to monitor surface properties, satellite multispectral data has recently emerged as a powerful method to analyze atmospheric content. However, the spectral resolution of multispectral instruments is poor, and methane measurements are typically very noisy. Methane data products are also sensitive to absorption by the surface and other atmospheric gases (water vapor in particular) and therefore provide noisy maps of potential methane plumes, that typically require extensive human analysis. Here, we show that the image recognition capabilities of deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data, with dramatically reduced false positive rates compared with state-of-the-art multispectral methane data products, and without the need for a priori knowledge of potential leak sites. Our proposed approach paves the way for the automated, high-definition and high-frequency monitoring of point-source methane emissions across the world.

AI could have a key role to play in climate change after the technology was used by scientists to identify greenhouse gas leaks in oil and gas wells. Research conducted at the University of Vermont used machine learning algorithms to predict whether the wells would emit significant amounts of methane – one of the most harmful gases contributing to global warming. It tested 38,391 wells in Alberta, Canada, and was able to determine which wells leaked – and those that didn't – with up to 87% accuracy. Professor George Pinder, who conducted the research alongside former doctoral student James Montague, said: "The big picture is that we can now have tool that could help us much more efficiently identify leaking wells. "Given that methane is such a significant contributor to global warming, this is powerful information that should be put to use." The analysis yielded a cluster of 16 traits that predicted whether a well would fail and leak. Researchers were given access to more complete information, including the fluid properties of the oil or natural gas being mined, for 4,000 wells. For these wells, the machine learning algorithm identified leaks with 87% accuracy. For a larger sample of about 28,500 wells, where the fluid property was not known and taken into account, the accuracy level was 62%. Companies in Alberta are required to test wells at the time they begin operating to determine if they have failed and are leaking methane. They must also keep careful records of each well's construction characteristics. Professor Anthony R Ingraffea – based at Cornell University's School of Civil and Environmental Engineering, in Ithaca, New York – is an expert in oil and natural gas well design and construction, but was not involved in the study. He said: "Provincial and state regulatory agencies never have enough inspectors or financial resources to locate, let alone repair, leaking wells.

The first Maker Faire of the year took place last weekend in San Mateo, California, and among the many thrillingly geeky sights and sounds on display was an indoor drone racing course set up by the Aerial Sports League, which bills itself the "only Major Drone Combat and FPV Drone Racing League." The Department of Energy was also attendance, and was so captivated by the Aerial Sports League's setup, that the agency recently posted a 360-degree video of the drone racing area to its Facebook page while lamenting the fact that "the U.S. Department of Energy doesn't race drones for fun like these enthusiasts." However, the DOE points out it is also intensely interested in drones a.k.a. "For example, drones can be used to detect methane leaks or help scientists select the fastest-growing strains of sorghum, an important bioenergy crop." That's the verbal equivalent of wearing socks and sandals, but just because the Department of Energy is more focused on scientific concerns like sorghum and methane leaks, doesn't mean they can't show off a gorgeous drone race track in 3D.