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AI Spotlight: Can Machine Learning Reduce Flaring?

What is flaring?

In the production of Oil and Gas, flaring is the process of burning off excess or otherwise unwanted gases. Typically, methane needs to be disposed of and flaring is a technique of disposing of the gas via combustion.

Methane is known to have a 25X impact over CO2 on the environment. Flaring burns off the unwanted methane which produces CO2 and water into the atmosphere. However, while flaring converts methane to the lesser of two evils in the form of CO2, the flaring process does not fully burn off all methane levels and thus the process is still a key contributor to the release of both CO2 and methane which are the leading causes of climate change.

What is Predictive Maintenance?

Predictive maintenance techniques utilize machine data and machine learning to help determine the condition of in-service equipment in order to estimate when maintenance should be performed.

The benefits of predictive maintenance are typically centered around extending the useful life of critical equipment while also saving on cost of maintenance and parts due to efficiencies gained in the maintenance schedule.

However, there are often overlooked indirect benefits as well.

Predictive Maintenance’s Impact on Flaring

In the production of oil and gas, drilling equipment and the wells themselves must be monitored and maintained.

Employing predictive maintenance can optimize the maintenance schedule to perform critical maintenance only when it is needed and not strictly based on a recurring schedule. In addition, early indicators of failure allow for adjustments to be made to equipment to avoid failure and push off maintenance. The indirect benefit to this is how the optimized schedules and extended equipment usability impact flaring.

Upstream companies are tasked with cutting down on flaring to fight climate change but some amount of flaring is routine and necessary.

When maintenance is to be performed on a well, the well must first be depressurized for safety. This process involves burning off the excess gas via flaring and in turn releasing pollutants into the atmosphere. While there are technologies in use and more in development to make use of the methane in ways that don’t require flaring, the reality is that flaring is a necessary part of oil production around the world.

However, even though flaring is necessary, it doesn’t mean that we cannot reduce it.

As noted, predictive maintenance optimizes the maintenance schedule and extends the usability of machines to reduce downtime. This optimization reduces the number of times maintenance is to be performed. This in turn reduces the number of times that maintenance based flaring needs to occurs.


Through the use of machine learning, Predictive Maintenance benefits go beyond extending the life of equipment and has the potential to have a positive impact on the environment as a whole. By deploying AI-based solutions, upstream oil and gas companies can reap the cost saving benefits of predictive maintenance while reducing their carbon footprint in the process.


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


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