To prevent the global temperature from increasing any more than it already has and meet the U.N.’s Paris Agreement temperature goal, we need to reduce global emissions by 7.6% every year by 2030. With oil …
To prevent the global temperature from increasing any more than it already has and meet the U.N.’s Paris Agreement temperature goal, we need to reduce global emissions by 7.6% every year by 2030. With oil and gas still ranking as the top energy source, this can feel like a pipe dream. But that goal is more obtainable than some might think, even in the oil and gas industry.
Energy giants are more motivated than ever to change their carbon footprint. Oil and gas companies are facing intense scrutiny from the public and governmental officials—both of whom are tired of hearing about new spills, fires, and other accidents. Meanwhile, companies are feeling the impact of climate change, dealing with events such as floods damaging refineries in the Gulf of Mexico and wildfires igniting almost a third of California’s oil fields. These factors are driving energy companies to double their efforts to improve the overall sustainability of their businesses.
Of course, the desire to create change and the ability to do so are two very different things. These companies face significant challenges in moving toward sustainability, and many struggle to prevail over them. Just the complexity of the organizations themselves is proving to be a substantial hurdle for many. Luckily, advances in cloud computing and artificial intelligence enable oil and gas companies to take tangible, substantive steps to overcome these systemic obstacles in several ways.
4 Ways Cloud Computing Can Help Take Sustainability to the Next Level
Though energy companies might already have treasure troves of data at their fingertips, its use is severely limited if there’s no way to turn the data into actionable information. Machine learning technology can analyze new and historical data to find ways to improve efficiency in several different ways. It can help determine when parts need maintenance to prevent breakdowns or accidents, find inefficiencies in the system that could hinder carbon capture efforts, and help match emissions with their source, just to name a few.
Without the cloud, if companies wanted to monitor and analyze the air quality of every location to track emissions, they would need to do so through the company network. For organizations with thousands of sites to watch, this would be expensive and cumbersome at best (and utterly impractical at worst). With IoT, however, this sort of plan is much more feasible, allowing organizations to capture all the information they need wherever they need it.
Sensors can be used not only to detect leaks and measure air quality, but also to reduce flaring (the burning of excess oil and gas), which produces methane—the second-largest greenhouse gas contributor to climate change.
By analyzing the data from sensors, AI can tell companies where and how to operate to reduce the amount of flaring. This includes providing methods for more efficient capture, reducing the need to burn off excess gas, and determining which formations have smaller amounts of gas to match the site’s capture capabilities. There are also products, such as flare.IQ, that use a combination of AI, IoT, and the cloud to promote flaring efficiency.
It might sound odd to suggest that cloud computing can reduce the emissions caused by computing, but that’s precisely what it does. One of the significant advantages of the cloud is that it scales much more efficiently than in-house server farms.
It’s estimated that switching over to the large-scale data centers of the cloud could reduce carbon dioxide emissions by one billion metric tons, if not more, over the next few years. Simply by making the switch to cloud computing, oil and gas companies could pay less, scale more efficiently, and reduce emissions all in one fell swoop.
Although the cloud won’t be able to solve all of the problems associated with climate change, it can make a significant dent in the efforts of oil and gas companies to reduce their global impact. By taking advantage of IoT to deploy cloud-based sensors and using machine learning to turn data into actionable information, energy companies can work to lower operational costs and reduce accidents while simultaneously cutting their emissions significantly.
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