by Markham Hislop, Contributor

How AI can help oil and gas producers reduce greenhouse gas emissions – and improve their business

Nov 16, 2021
Artificial IntelligenceGreen ITMining, Oil, and Gas

Reducing emissions is an important part of better operational performance that includes higher efficiencies and lower costs.

A heart-shaped leaf lies on a circuit board. [Green IT / environmental impact / climate change]
Credit: Weerapatkiatdumrong / Getty Images

In Canada, oil and gas production accounts for a quarter of national GHG emissions and is a significant emitter in the United States. As concern over global warming grows, regulations are tougher, investors are more demanding, and even bankers want assurance that producers are managing their “climate risk.” Lowering greenhouse gas (GHG) emissions from oil and gas production is smart business. Done properly, reducing emissions is an important part of better operational performance that includes higher efficiencies, lower costs, and longer well runtimes.

Artificial intelligence and optimization

A typical engineer oversees around 250 wells. There are only so many hours in the day and who has the time to crunch all that data? Keeping production online, not optimizing, is often the first priority. That leads to a “set it and forget it” mentality and poor performance.

A Texas operator with 200 wells followed this approach, leading to a sharp drop in operating cash. The solution was to let artificial intelligence crunch the data, which revealed that two-thirds of the rod-lift wells were over-pumping. Automating setpoint management using artificial intelligence lowered electricity consumption by 11%, with GHGs emissions falling 13% to a level that aligned with corporate sustainability goals.

Work remotely, lower emissions

During the COVID-19 pandemic, staff are able to maintain full control while working from home (very difficult with on-premises systems) because data is stored in the cloud. The data can then be accessed remotely.

Cloud computing has been adopted slowly for oil and gas automation because companies worry about security, data ownership, and loss of control over the data. But the value of AI-enabled optimization and maintaining continuity during difficult business disruptions has convinced executives and owners of its value.

Cutting down on travel is another way to lower emissions. AI-enabled automation reduces operator travel to the field.

Predictive maintenance

Keeping the workover rig off the well is job one for producers. Failures cost money. Workovers are expensive and the well isn’t producing and generating revenue. Predictive maintenance using artificial intelligence is an increasingly popular solution to this problem.

Using historical and current data, the software can tell when a piece of equipment is likely to fail. Repairing or replacing before a failure occurs keeps the rig away from the lease. “Prescriptive” analytics goes one step further, explaining why the equipment broke and where to order the replacement part.

Artificial intelligence is about bottom line

Oil and gas has always been a cyclical business. Producers ride out the valleys and hope to profit during the peaks. They can’t control prices, but they can control costs.

Digital technologies using artificial intelligence enable companies to analyze data to spot trends they never would have noticed before and to automate processes. Experienced staff are then freed up to resolve high-value problems like reducing greenhouse gas emissions.

The company lowers operating costs and meets corporate sustainability objectives.

Markham Hislop is a Canadian energy and climate journalist who conducts video interviews with global energy experts. He also hosts the Energi Talks podcast, writes the Markham On Energy energy politics analysis columns, and writes about the energy future. He is frequently interviewed on Canadian radio about energy transition issues and is CBC Radio Canada’s “Green Switch” columnist.


This post is brought to you by Ambyint and IDG. The views and opinions expressed herein are those of the author and do not necessarily represent the views and opinions of Ambyint.