Natural Resources

Energy and Climate Action

With economic recovery from the coronavirus pandemic on its way and global energy consumption predicted to rise again once it does, it’s never been more important to manage energy consumption wisely.

Whether we’re making better use of fossil fuels or finding more ways to make renewables more economically viable, artificial intelligence can help in the search for energy solutions.


Our AI and Machine Learning for Energy and Climate Action

Our experience in AI and machine learning solutions include the following applications:

Pipeline Health Monitoring

Pipelines are historically the safest way to transport oil and gas, but they’re politically controversial because of the potential impact of leaks. More oil and gas operators are trying to improve the odds with better monitoring that enables rapid response in case of damage.

Machine learning solutions that use millions of multi-spectrum satellite images of transmission pipelines can quickly flag any day-to-day variations that could signal a leak.

The end results are safer and more reliable energy delivery for consumers, improved bottom lines and public images for operators, and a healthier environment for everyone.


Grid Feed Optimization can help utilities improve grid resilience, response to demand bumps, and prioritization of renewables when demand drops (as renewables are cheaper to run than fossil fuel burning plants). When demand peaks, the right AI can tell utilities which independent sources they should draw from, like the generators used by private industry and independent facility microgrids. It all adds up to a suite of cost savings solutions.

In the long term, demand planning will link to factors like population growth, historical weather patterns, projected weather pattern shifts, and even socioeconomic factors like changes in consumer behaviour. It’s a complex interplay of conditions that requires machine learning to process effectively.


Predictive Analytics for Oil and Gas Pricing

Oil pricing is based on two things, density and sulphur content. With density, there’s a Goldilocks zone between being too light and too heavy, and operators lose money at both extremes. With sulphurs, operators are penalized for having too much and rewarded if they have less. Density and sulphur content will both vary from source to source, and again over time.

Getting an optimal price point depends on blending oil from different sources to get a better selling price. Where AI solutions can help is in processing the datasets that will find the sweet spot of optimum pricing


Climate Action

Climate-related disasters are taking an increasing toll on economies and communities. The United Nations reports that 2019 was the second warmest year in the warmest decade ever, in the decade from 2010 to 2019.

As one of the UN’s 13 sustainable development goals, climate action has a lot of potential metrics, like shoreline erosion verification, that AI systems can help bring into focus. solutions are designed to provide tangible data collection and analysis that will help us understand problems better and find new ways to solve them.


Making A Difference With Real Data
Let us handle the design.

As a planet, we will still rely on some use of fossil fuels in the years to come. But we can and should do better in terms of transporting them safely and using them responsibly. There are thousands of potential applications of AI to the problem of ensuring enough energy for all our needs, while taking action on climate and protecting our natural heritage for future generations. can help you determine the right way to leverage machine learning solutions to help organizations do more.


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