How a natural gas company is using machine learning in natural gas exploration

The modern business world is becoming increasingly technology-driven and machine learning (MD) is currently at the forefront. While one might not inherently link natural gas and innovation together, there is one company already leading the charge.

MCF Energy is leveraging ML to optimize its drilling process and identify areas of high potential in a more sustainable and efficient way.

And ultimately to reduce risks and challenges previously associated with the natural gas industry.

Using this as a starting point, there are other opportunities for ML in the sector, that can be replicated to increase safety and overcome challenges, like:

Data Analysis in Super Mode

The drilling process has been optimized ten-fold thanks to the shift from human analysis to data examination via modern software. Human error in data analysis is a well-known and understood issue, and while previously accounted for, it’s rhetorical to say that we are better off without them.

With more accurate data sets being relied on and stronger inferences being made thanks to technology, companies like MCF Energy regularly experience success rates that reach above 80%.

Knowing Where to Dig From the Get-Go

Earlier this year, the company also identified key location sights for spudding in record time. Using data analyzed by machine learning software to reliably pinpoint where under rock formations, oil and gas is present, tells technical experts where exactly they should dig. Including previously hard-to-reach locations.

This means operations are far more likely to be safer, shorter, and more accurate while also allowing extraction to take place in previously inaccessible areas.

Environmental Benefits

We’re en route toward full electrification and regular use of renewables, but during this transition process (given we still have some time to go), it’s important to see energy companies take the transition seriously.

One way companies can demonstrate this is by using superior techniques and best practices that stem from modern technology.

While machine learning helps with accuracy and precision, its dual benefit is reducing error. Error derived from the use of outdated techniques is common and can lead to drilling and extraction at the wrong location sites. Reduced awareness around potential hazards is also a challenge and one that tends to surface after the process has begun. If we are able to mitigate challenges like this long before projects start, lives can be saved and our environment better sustained.

But Safety, first.

Natural gas companies that utilize machine learning are able to take advantage of enhanced safety measures and predictive maintenance capabilities. By analyzing data from sensors, drones, and other sources, machine learning algorithms can detect potential safety risks before they become major issues. This proactive approach plays a key role in accident prevention.

Predictive maintenance also helps companies identify and resolve equipment failures before they occur, reducing downtime and improving efficiency.

New-gen Renewable energy companies are basking in that ML new new

Other in the energy sector are also leveraging machine learning to improve and optimize operations. Enel, a multinational energy provider, uses machine learning to predict wind power output and optimize its renewable energy operations. With ML, Enel can better anticipate changes in weather patterns and adjust its operations accordingly.

While we know machine learning has widespread implications outside the energy sector and is being used in a range of applications, its use in the energy sector is showing clear signs of benefit - and it’s safe to say we’ve just scratched the surface.