Today, AI is helping the oil and gas industry chart its future course. Since no previous sources have provided an in-depth look at the impact of AI among the leading oil and gas companies, we set out in this week’s research to help answer questions that oil and gas leaders are asking:
What types of AI are applications currently in use by leading oil and gas companies such as ExxonMobil and Shell?
What (if any) results have been reported on AI applications implemented by leading companies in the oil and gas industry?
Are there any common trends among their innovation efforts – and how could these trends affect the future of the oil and gas industry?
This article seeks to provide a comprehensive look at applications of AI by the five leading oil and gas companies. Our ranking of companies is based on the Forbes’ 2017 Global 2000 ranking of the world’s biggest public companies.
Through facts and figures we aim to provide pertinent insights for business leaders and professionals interested in how AI is impacting the petroleum industry.
Prior to exploring the applications, we’ll present the common patterns that emerged from our research in this industry.
Artificial Intelligence in Oil and Gas – Insights Up Front
The most popular AI applications from the top five industry leaders currently appear to be:
Intelligent robots – Robots designed with AI capabilities for hydrocarbon exploration and production, to improve productivity and cost-effectiveness while reducing worker risk (see ExxonMobil and Total below)
Virtual assistants – Online chat platform that helps customers navigate product databases and processes general inquiries using natural language (see Royal Dutch Shell below)
In the full article below, we’ll explore the AI applications of each company individually. We will begin with ExxonMobil, the #1 ranked company in this industry based on the Forbes’ 2017 Global 2000 ranking of the world’s biggest public companies.
Among its ongoing collaborative efforts with approximately 80 universities in the U.S. and abroad, In December 2016, ExxonMobil announced that it is working with MIT to design AI robots for ocean exploration. Brian Williams, an MIT professor and a designer of the AI software that helped create NASA’s Mars Curiosity Rover is a key member of the project team.
While the business advantage of using AI in deep-sea exploration may not be immediately apparent, the company aims to apply AI to boost its natural seep detection capabilities. Natural seeps occur when oil escapes from rock found in the ocean floor. An estimated 60 percent of oil underneath the earth’s surface in North America is due to natural seeps. Robots with the ability to navigate these oceanic regions and detect oil seeps can contribute to protecting the ecosystem and serve as indicators for robust energy resources. It is unclear specifically when ExxonMobil’s ocean exploring AI robots are expected to be deployed.
A visual depiction of natural oil seeps (source)
As a founding member of MIT’s Energy Initiative, ExxonMobil has committed a reported $25 million over 5 years to support energy research conducted by MIT faculty and staff. While the company has not published the total amount invested across its 80 university collaborations, we can gain some insight from the following published figures:
Princeton University: $5 million over 5 years
The University of Texas at Austin: $15 million
“Our goal is to have these submersibles embody the reasoning of the scientists that program them. 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. They need to have some common sense and the ability to learn from their mistakes.” – Professor Brian Williams, MIT
ExxonMobil’s MIT robotics collaboration (source)
Through its partnership with the MIT Energy Initiative and related efforts, ExxonMobil has made energy efficiency and the exploration of new energy sources a core focus of its business priorities. According to its 2016 annual report, the company has reportedly invested roughly $7 billion since the year 2000 on R&D and “deploying emissions-reducing technologies.” The company does not itemize allocations for these technologies and specifics on AI were not published.
Royal Dutch Shell
In August 2015, Shell announced that it would be the first company in the lubricants sector to launch an AI assistant for customers (an anomaly in terms of applications of artificial intelligence in oil and gas). Normally, customers searching for lubricants and related products must navigate a large database in order to find the ideal product(s) they are searching for. Shell aims to use its avatars, Emma and Ethan, to help customers discover products using natural language.
The Shell Virtual Assistant functions through an online chat platform through the company’s website. Examples of information that the system can provide include where lubricants are available for purchase, a range of available pack sizes and general information regarding the technical properties of specific products.
To provide context, the company claims that its Shell Virtual Assistant:
Handles over 100,000 data sheets for 3,000 products
Provides information on 18,000 different pack sizes
Understands 16,500 physical characteristics of lubricants
Matches Shell products to 10,000 competitive products
The Shell Virtual Assistant is only currently available in the U.S. and U.K. but also complements four other Shell services including Shell LubeMatch which reportedly provides “over two million product recommendations for Shell customers” annually and is accessible across 138 countries in 21 languages.
An infographic representing the claims made about Shell Virtual Assistant – from Shell’s website
We were able to access the virtual assistant on the company’s website. In a note to users posted above the platform, Shell states that the virtual assistant is still in pilot mode and that efforts are ongoing to increase the knowledge of the virtual assistants and to monitor their effectiveness.
At tech emergency, we have become increasingly wary of “chatbot” and “virtual assistant” efforts that present an innovative story without a substantive business application. It is difficult to assess the genuine business value of the Shell Virtual Assistant at this time, but we tend to air on the side of caution, and we encourage our business readers to do the same (we have collected a series of chatbots that do appear to be driving business value today, and we highlight them in our “7 chatbot use cases” article).
It behooves any company to present an exciting and innovative front in the press – and chatbots seem to be a “low hanging fruit” for supposed AI applications. This is by no means a warning that we specifically state for Shell – all press-facing technology initiatives serve the purpose of molding perceptions about the company (the goes for every industry). We do our best to dig for the genuine business ROI of AI applications, and we advise our readers to approach applications and press releases with skepticism, bearing in mind the motives of the companies behind them (which do not have to be malicious to be misleading and falsely optimistic).
Similar to Shell LubeMatch, the company is also looking to expand the service to other countries and languages.
Shell’s R&D expenditures in 2016 totalled $1.014 billion. While specifics on AI were not reported, according to its Investor’s Handbook, R&D priorities are focused on “improving the efficiency of its products, processes, and operations”, and there is a concentration on developing technologies which support low-carbon energy.
Shell’s innovation in collaboration with Subsea 7 has created an Autonomous Inspection Vehicle
that claims to provide safer and better inspections – at a significant cost savings
In the future, the company reportedly seeks to integrate AI and automation into its facilities. Shell envisions that automated robots will be able to take over routine observational tasks and data gathering currently conducted by human employees. The company reportedly integrated a virtual assistant called Amelia into its business model to more efficiently respond to inquiries from suppliers regarding invoicing.
Shell believes the future of AI in its industry will see a significant increase in unmanned and automated facilities.
China Petroleum and Chemical Corp. (Sinopec)
Sinopec has hinted at the role of AI in moving innovation forward in the oil and gas industry. The company boasts a long-term plan to roll out construction of 10 intelligent plants with a goal of a 20 percent reduction in operation costs.
On the manufacturing front, Huawei (Chinese telecom company) in April 2017 announced a collaborative effort involving Sinopec to design what is described as a “smart manufacturing platform.”
The platform description highlights AI as one of 8 core capabilities of the platform which aims to deliver a centralized method of data management and support integration of data across multiple applications used to manage factory operations.
AI would serve to establish rules and models that would inform how data is interpreted and offer opportunities for identifying valuable insights to improve factory operations. Huawei did not specify a timeline for when Sinopec is expected to fully implement the platform.
Hydrocarbon exploration, the ability to map and identify oil and natural gas deposits beneath the earth’s surface, is a growing area of focus in the oil and gas industry. However, more innovative and environmentally-friendly methods of achieving improved effectiveness and efficiency are needed in the field. Environmental conditions are increasingly challenging for workers conducting hydrocarbon exploration thus technology capable of handling the task while retaining optimal functionality is highly desirable.
In an effort to establish what is described as the “first autonomous surface robots able to operate on oil & gas sites,” Total launched an international competition in December 2013. Total’s ARGOS challenge (Autonomous Robot for Gas & Oil Sites) was narrowed down to five teams hailing from Europe, Asia and South America who were provided with a maximum of three years to finalize their prototypes. For each of the 5 teams, Total provided a maximum of €600,000 (approximately $707,376) to support research and design, and a single prize of €500, 000 (approximately $589,522.50) for the winning robot.
AI was a key component of how the robot would function. Total expected that competitors ensure that their robots were able to deliver reports encompassing real-time data collection related to inspection points (locations where exploration is taking place) and analyses around the effectiveness of the locations of interest.
Total established key goals for the ARGOS robot:
The ability to carry out inspections, during the day or night, which are currently performed by humans.
The ability to detect abnormal equipment activity and intervene in an emergency. Examples may include simple equipment malfunctions or more high-risk situations such as gas leaks.
In May 2017, Total selected ARGONAUTS designed by a team from Austria and Germany as its winner. Total retains exclusive intellectual rights to the technology behind the ARGONAUTS robot for a period of five years. No further announcements have been made as to when the company will begin implementing ARGONAUTS.
Within its Exploration and Production segment, Total reports that over half of R&D allocations are focused on improving exploration capabilities; hydrocarbons and robotics are specifically mentioned. Innovation and R&D expenditures for oil and gas activities totaled $689 million in 2016.
(Readers with a specific interest in robotics and vehicles for the heavy industry may want to listen to our heavy industry-focused interview with Dr. Sam Kherat on our AI in Industry podcast.)
In June 2017, Gazprom and Yandex (described as Russia’s leading internet company) entered into a cooperation agreement for the implementation of new projects in the oil and gas industry. The two companies plan to tap into AI and machine learning to roll out their prospective initiatives.
Specifically, the collaboration is expected to focus on:
Drilling and well completion
Modeling oil-refining strategies
Optimizing other technological processes