Guest post by By Sean Donegan, President and CEO, Satelytics
When it comes to the negative environmental effects of oil and gas production, methane often takes center stage – whether in the board room or in the media. However, while methane is indeed a significant issue, produced water is arguably just as pressing a concern.
Produced water—also known as brine—is one of the natural byproducts of oil and gas production, created when shale rocks release hydrocarbon molecules. Some experts have called it the greatest challenge facing the oil and gas industry today—and the problem is only intensifying.
Right now, the Permian Basin is responsible for generating 11 million barrels of produced water daily. The high salinity levels contained in this produced water—not to mention the many other contaminants—pose threats to ecosystems and water bodies that cannot be overlooked.
What is needed is a proactive approach to identify water leaks in their infancy, before they can gather momentum and lead to an excess of produced water. Luckily, one solution—AI-powered geospatial analytics—has proved itself more than up for the task.
The Problem of Produced Water
Before examining how AI is overhauling the state of produced water, it’s essential to understand the full scope of the problem.
Produced water is, needless to say, a serious pollutant: it can contaminate drinking water, streams, and agricultural water, and—when its discharge is uncontrolled—it can kill plants and aquatic life. Worse yet, polluted water can contain toxic substances like arsenic and radium as well as carcinogens and other chemicals—drastically increasing the nature of the threat.Â
While produced water can be treated, treatment presents additional challenges. The exact properties of produced water are in constant flux, which renders stable treatment methods impossible—those engaged in water treatment need to constantly identify new solutions, making a costly process even more expensive. Moreover, convincing the public to accept treated water use is challenging in itself.
Given these hurdles, treatment alone isn’t sufficient. Any effective approach to solving our produced water problem needs to be preemptive: identifying potential problem areas before they’re able to spin out of control. On this front, one solution has in recent years moved to the front of the pack: AI-powered geospatial analytics.
Figure 1: Leaks can happen at remote, unmonitored sites leading to severe remediation challenges.
Figure 1: Leaks can happen at remote, unmonitored sites leading to severe remediation challenges.
Traditionally, leak detection methods have been a costly, time-consuming, and labor-intensive process: assessors would trawl the relevant areas and manually take samples of potential problem sites. Inspectors must physically survey vast areas, creating blind spots and often detecting issues too late for meaningful action.
AI-powered geospatial analytics has proven to be an effective strategy to this problem. Using satellite imagery and advanced algorithms, it can quickly survey relevant areas, analyze for signatures of chlorides and other chemicals often found in produced water, and—if necessary—quickly alert oil and gas personnel to relevant problems.
Identifying liquid leaks in gathering and processing areas is just one example of how this technology is already being deployed across the world. Gathering and processing areas are prone to these kinds of leaks, as well pad equipment and gathering lines often don’t receive the same level of attention as interstate pipelines. Virtually every oil and gas company has lost money remediating leaks of this nature; AI-powered geospatial analytics are helping to ensure that they won’t have to do so again.
Figure 1. Spot produced water leaks before they cause untold damage.
Case Study: North Dakota’s Spill Problem
To understand how this technology operates in real-world contexts, we can turn to the example of North Dakota. Back in 2017, North Dakota Governor Doug Burgum met with 75 pipeline operators for a candid conversation about ongoing spill problems (at that time, North Dakota was widely known as the “Spill State”). Burgum, a former senior executive at Microsoft, was alert to the potential of technology to fix this state of affairs and encouraged the pipeline operators to investigate technological solutions.
This meeting spurred the formation of an oil and gas producer consortium in the state, which pledged itself to find a new approach to identifying liquid leaks across their vast infrastructures (an area totaling over 10,000 square miles!). This technology would need to carry two burdens. One, it would have to quickly and effectively identify leaks. Two, it would need to distinguish crude oil leaks from produced water leaks, while proving effective in all seasons. (Eventually, the consortium would also move on to methane gas measurement.)
The consortium adopted AI-powered geospatial analytics, applying it to a 1,100-square-mile area of interest (2,012 linear miles of pipelines). In just the first year, 176 North Dakota leak detections were reported to the consortium, with 50 more the following year. Excitingly, the technology was able to identify not just major leaks but also subtle ones—for instance, a tiny leak of hydraulic fluid from a bulldozer. It was in the project’s second year that the consortium turned its attention to distinguishing produced water from crude oil leaks—and the results were just as impressive.
What North Dakota’s oil and gas producers learned reflects a broader industry trend: AI-powered geospatial analytics is transforming leak detection. Manual inspections retain some relevance, but the speed, accuracy, and coverage offered by AI make it indispensable for producers focused on addressing produced water challenges. Excitingly, this process is only getting started: it is clear that, in the short-term and the long-term, the detection capabilities of this technology are only going to grow.Â
About the Author:
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.
With his energetic leadership style, Sean has always believed in building talented teams whose members are laser-focused on problem-solving, results, and financial objectives. These qualities were illustrated during his 15-year tenure as CEO of Westbrook Technologies, Inc. where he transformed a failing enterprise into a highly profitable document management software global leader with customers in 52 countries. Sean earned an undergraduate degree from the University of London and a postgraduate professional qualification from the Chartered Institute of Management Accountants. Sean lives in Hunting Valley, Ohio.
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