Artificial Intelligence: Next Productivity Revolution in the Oil & Gas Industry

The oil and gas industry is not typically seen as an artificial intelligence (AI) innovator, but that perception may soon change. A Markets and Markets report estimates the value of AI in the oil and gas industry will reach $2.85 billion by 2022, growing at a compound annual growth rate of 12.66 per cent.

Artificial intelligence has a number of potential applications in the oil and gas industry, from surveying to planning and forecasting, and facility management to safety. ExxonMobil, the world’s 9th largest company by revenue, made headlines recently when the company announced a partnership with the Massachusetts Institute of Technology (MIT) to design AI robots for ocean exploration, which it wants to use to improve its natural seep detection capabilities.

“Our goal is to have these submersibles embody the reasoning of the scientists that program them,” said Brian William, who leads the Model-based Embedded and Robotic Systems group within the Computer Science and Artificial Intelligence Laboratory at MIT. “You want the explorer to do the science without the scientist there. They need to be able to analyze data, keep themselves out of harm’s way and determine novel solutions in novel situations that go beyond basic mission programming.”

Another oil and gas company that sees artificial intelligence as a key to unlocking the next productivity revolution in the industry is Gazprom, the largest supplier of natural gas to Europe and Turkey. The Russian giant has entered into a cooperation agreement with Yandex, which is sometimes described as Russia’s Google, to explore various potential artificial intelligence applications.

Alexander Khaytin, executive director of Yandex Data Factory, said he expects the oil and gas industry to implement solutions based on machine learning and AI because the industry handles “massive volumes of data” and that the “easy solutions for optimizing production and business processes have long since been implemented.”

A few years ago, Shell launch an AI assistant for customers. Represented by avatars Emma and Ethan, Shell’s AI assistant is available around the clock to answer any common lubricant-related question within seconds. According to Shell, the assistant handles more than 100,000 data sheets for 3,000 products, understands 16,500 physical characteristics of lubricants, and provides information on 18,000 different pack sizes.

“Shell Virtual Assistant is the next step in our delivery of great support—it is like having a technical expert at your fingertips, ready to answer your technical questions anytime,” said Roger Moulding, vice president of global marketing at Shell Lubricants.

But AI assistants are not limited to customer support. They can also be used to transform safety meetings by identifying key details, providing actionable summaries, and removing the need for multitasking during important discussions. With an AI assistant for safety meetings, oil and gas companies can minimize or eliminate the duplication of efforts and enjoy greater visibility into problems. Such assistants can help workers make safety-critical decisions in real time and handle the growing complexity of engineering systems.

There are many known artificial intelligence applications in the oil and gas industry, and many more are still waiting to be discovered. The leaders in the industry have the budgets and talent necessary to implement artificial intelligence solutions developed by prominent AI companies and in-house IT staff alike, and some of them are reaping the numerous benefits of artificial intelligence already. Smaller players should follow suit to avoid being left behind.

Dave Damer is an engineer and tech entrepreneur with more than 27 years of leadership experience. In 2017, he founded Testfire Labs, an Edmonton, Alberta software company that uses machine learning and artificial intelligence to build productivity solutions that modernize the way people work. Testfire's flagship product is Hendrix.ai, an AI assistant that transcribes meeting summaries and action items, audits meeting history, and drops it all into a searchable dashboard for organizational and productivity insights.