On a daily basis, we are reminded of the dangers posed by extreme weather. While wild fires and hurricanes have dominated the news of late, prolonged cold snaps can have ruinous effects on oil and gas infrastructure. The effects of cold weather on oil and gas pipelines are extensive and left untended—potentially hazardous. Winter stress affects metal, fiberglass, and plastic, leading to an increase in leaks and releases.
The challenge is that many energy organizations today are tasked with maintaining aging and widely distributed pipeline infrastructures. Manually detecting leaks in such a massive enterprise is effectively impossible. No company has the resources to patrol every inch of their pipeline network 24/7—and even if they could, many early-stage leaks ranging from methane (gas) leaks, liquid hydrocarbon (crude oil) leaks, and produced water (brine) leaks would remain undetected by mobile instruments.
That said, oil and gas producers must identify and fix potential leaks as rapidly as possible. The trouble is that these leaks are often so small—or so inopportunely located—that even trained teams have trouble identifying them. The consequences of these blindspots—for oil and gas producers, for the customers they serve, and for the environment—can be immense.
It is for this reason that, in recent years, a new technology has emerged as a go-to identification tool for oil and gas producers: AI-powered geospatial analytics. Equipped with this technology, oil and gas producers are identifying leaks at a dramatically increased rate—and staving off potential catastrophes in the process.
When discussing oil and gas operations, it is important to keep in mind the sheer scope of the infrastructure: ExxonMobil, for instance, has 16,000 miles of pipes in the US alone. Patrolling thousands of miles of pipes daily would require a small army working around the clock. Even if a company could manage such a task, these teams would still inevitably fail to detect early-stage leaks, which can be much more challenging to identify with conventional tools.
This is where AI technology is helping and making a considerable difference in leak detection.
AI-powered geospatial analytics are a relatively new technology, but they are already making a massive difference in the oil and gas industry—for instance, they played a significant role in the 85% reduction in methane emissions in the Permian Basin between 2011 and 2021.
This process starts with high-resolution satellite imagery: multispectral and hyperspectral imagery gathered from satellites, taking in the full sweep of an oil and gas producer's pipeline operations. But the beating heart of this process is the algorithms that make sense of the imagery.
These cutting-edge analytics can precisely identify, locate, and quantify critical problem points like methane emissions, leaks, or right-of-way encroachments across unimaginably vast terrains. Crucially, this technology is granular. It can locate micro-disruptions at the component level while they are still in their infancy, allowing the relevant companies to take action to mitigate potential larger consequences. Organizations can now take in the massive amounts of information contained in high-resolution satellite imagery and—within hours— provide oil and gas producers with actionable, up-to-the-minute insights on what's going wrong with their operations. These insights are remarkably precise, identifying risks within just a few square feet.
By deploying this technology, oil and gas producers can identify leaks almost in real-time, and can take action before leaks or corrosion spiral out of control. Companies can also put their resources to use more effectively, sending repair teams only to those locations that require actual assistance. The benefits here—for the oil and gas producers themselves, for the surrounding communities, for the general health of our atmosphere—are incalculable.
What is important to remember is that, when it comes to leak detection, time is of the essence. A simple two-week gap in leak identification—not at all unusual with conventional identification methods—can cost a business millions of dollars (if not more). Fines alone are a major consideration here, as governments across the world continue to tighten environmental regulations to keep up with the pace of climate change.
For instance, in 2023, AI-powered geospatial analytics technology was able to identify a difficult-to-detect leak for an oil and gas producer — a leak that traditional methods wouldn’t have discovered for another two weeks. Without intervention, the leak could have led to remediation costs exceeding $10 million. Since the program's launch in October 2022, the region has detected 67 produced water or crude oil leaks over a 25-month period.
Likewise, operators in the frigid Bakken region also started using AI-powered geospatial analytics to improve the detection of liquid leaks on gathering lines. The result: geospatial analytics was able to detect brine and hydrocarbon signatures on almost any surface.
The benefits to the environment cannot be overstated. Identifying methane emissions rapidly—and allocating repair resources effectively—allows oil and gas producers to prevent methane from interfering in the wetlands, forests, and other natural environments that are so crucial to sustainability and public health efforts worldwide.
What the rise of AI-powered geospatial analytics speaks to, ultimately, is the power of data-driven decision-making. Data-driven decisions have overhauled virtually every other industry in the last few decades, and it is crucial to apply the same techniques to something as important as environmental health. And this is why AI-powered geospatial analytics are rapidly becoming standard practice in the field. In the coming years, expect this technology to play even more of a role in keeping our communities and ecosystems safe.
Sean Donegan is the President and CEO of Satelytics. He brings over thirty years of technology and software development experience to the company. A dynamic leader, Sean’s career has been focused on building companies through creativity and innovation, recruiting highly effective teams to solve customers’ toughest challenges. Sean founded or owned four successful software companies, most recently Sean Allen LLC which was focused on predictive analytics in the oil & gas marketplace.
