AI Spotlight: Leak Detection and Repair (LDAR)
Updated: Jan 4
Reduce Methane Emissions and Cut Costs with AI and Machine Learning
A study by the Environmental Defense Fund determined that over a 5-year period, methane leaks in the US were 60% higher than originally estimated by the EPA.
The wasted gas is enough to fuel 10 million homes for a year, lost gas that’s worth an estimated $2 billion. In addition to the economic impact, the leaked methane is 80x more harmful than CO2 during the 20 years after it is released into the atmosphere.
The resulting impact leads to devastating economic loss by both consumers and the Oil and Gas industry as a whole while also accelerating climate change.
Leak Detection and Mitigation with AI
The federal government, as well as the industry as a whole, is trying to do their part in reducing emissions. Successfully doing so will require a multi-prong approach. One such approach will be focusing on leak detection and repair.
With the acceleration of sensors and Industrial IoT, oil and gas companies are rich with data. This data can be combined with machine learning to detect leaks in an accurate and timely manner, allowing for quick remediation.
Technology now allows for numerous ways to detect the colorless and odorless methane gas. On the more expensive side, cameras deployed in the field or even from space can be utilized to monitor for and detect large methane leaks. In June 2022, researchers at Spain’s Polytechnic University of Valencia, said they uncovered the latest known super-emitter event at an oil and gas platform in the Gulf of Mexico.
40,000 tons of methane were discharged over a 17-day period in December 2021. Researchers said the release may never have been known to the public if not for the fact that it was captured by a European Space Agency satellite.
However, while this technology has proven effective, it is mainly accurate for only the largest of leaks and difficult and expensive to deploy at scale.
A Machine Learning Approach
A more scalable approach is to monitor pipelines and equipment via low cost pressure, flow, and even vibration sensors. Through machine learning, a computer can learn what normal operation of a pipeline or a machine looks like. With this trained model, systems can be monitored in real-time to look for a change in the data profile that is indicative of a problem such as a leak.
For example, simply by monitoring pressure along different points on the line, a machine learning algorithm can find patterns of normal in order to trigger alerts when the pressure or pressures go above or below a learned threshold.
More importantly, for systems that have different states such as numerous valves, machine learning can learn normal based on the state of the valves being opened to find patterns that humans and statistics would otherwise struggle with.
To learn more about how Elipsa’s no-code AI platform can detect leaks and help cut down on costs and methane emissions, schedule a call today!
Elipsa is a leader in delivering fully automated, scalable AI solutions for industrial IoT applications. Elipsa’s Intelligent Monitoring Platform seamlessly deploys across any workflow on the edge or in the cloud, increasing the availability and output of critical equipment. Elipsa’s self-training AI models and bolt-on approach enable AI deployments that are simple, fast, and flexible. To learn more, please visit https://www.elipsa.ai or setup a call to learn more