Can Artificial Intelligence be a Game Changer for Upstream Oil and Gas?

Whether it is apparent or not, Artificial Intelligence (AI) will be ubiquitous in nearly all products and services within the next decades.  

To pivot with our industry and prepare for the AI wave, LOGIC is integrating AI into its solutions and delivery strategy today. As part of our AI journey, our team members recently completed an MIT Management Executive Education short course in Artificial Learning. Through this course, we enhanced our understanding and our ability to support our clients in identifying and delivering on appropriate, sustainable and business-driven AI solutions for the energy industry.

The information shared below is a recap of what we learned applied to the upstream oil and gas sector.

AI, Machine Learning, Natural Language Programming, and Robotics

Course Director, Thomas Malone, Professor of Management and Founding Director of the MIT Center for Collective Intelligence, has distilled decades of research and definitions down to an intuitive definition of AI as “machines acting in ways that seem intelligent.” Within that area, he distinguishes three broad areas of AI: Machine Learning, Natural Language Programming and Robotics (which also includes RPA – Robotic Process Automation).

Can Artificial Intelligence be a Game Changer for Upstream Oil and Gas?

AI can be a great advantage for upstream oil and gas companies. As our industry continues to innovate, AI working in conjunction with people can help automate, optimize and make for safer working conditions in the emerging supply chain approach to oil and gas exploration and production. The industry can leverage Machine Learning (ML), Natural Language Programming and Robotics to reduce costs, differentiate and gain competitive advantages. “The next generation of competitive advantage in the energy marketplace will go to forward-thinking players who invest on digital IoT and artificial intelligence capabilities,” says Archie W. Dunham, chairman emeritus and former independent non-executive Chairman of Chesapeake Energy in Oklahoma City and retired ConocoPhillips Chairman.

The upstream energy sector is a fascinating place to work. Geoscientists search the globe for new, economically viable deposits while engineers design, construct and operate the means of extraction, sometimes miles underwater. Making this enormous set of processes more efficient and improving safety is an evergreen task for such globetrotting organizations.

AI and machine learning can help these organizations take historically time and labor-intensive tasks and turn them into always available, real time analysis. For example, imagine a single dashboard delivering integrated data on block models, exploration drilling, and control measures. Having that information at your fingertips will improve decision making in time sensitive situations like choosing areas for drilling and blasting.

A Shared Vision for AI

Achieving a vision of improved efficiency and safety through AI or machine learning will be challenging. The oil and gas sector, like many more mature industries, can often fall behind the curve on the latest technology trends and adoption of new and valuable tools. Adding to the difficulty, AI should represent a shared discipline that straddles the business and IT groups, but there is often a chasm between the two. The business must drive the questions that need to be answered for the increased value to the company (“pick the problem”) whereas IT must be able to support the technical solution. Further, the business and IT must be able to work with the selected technology to improve the AI outcomes over time.

Real Life Applications

Examples of how Machine Learning (ML), Natural Language Programming (NLP) and Robotics can be applied to the oil and gas space are provided below.

Machine Learning

Competitor activity is a critical area that upstream oil and gas companies monitor. Upstream companies are now using ML to monitor activity via image recognition and object classification automatically in areas of interest. This application supports quick data-driven decisions on competitive activity, leading to optimization of leaseholds - ultimately reducing costs and improving revenue.

Natural Language Programming

Land lease document processing is a strong candidate for NLP. Today, oil and gas companies spend many human hours on antiquated systems reviewing and manually entering the various clauses and dates associated with land leasing necessary to not “lose” a lease. Using NLP to automate this process can reduce costs while improving data quality and reducing errors.


Surprisingly, this may be the quickest jump for some oil and gas companies as the use of robotics is becoming a standard with dangerous manual field inspections. Autonomous drones are leveraged to fly pipeline, rigs, and other field facilities in place of humans. Robotics dramatically reduces human risks related to environmental dangers. An Increased safety record helps reduce business costs and can differentiate companies in this commodity marketplace. Specifically, when it comes to field work, contractors want to partner with companies that are proactive and innovative when it comes to safety.

3 Strong AI Applications You Can Implement Today

There are many different ways to apply AI technologies to the oil and gas sector. The common factor is that AI can help oil and gas companies lower costs, improve safety conditions and make more accurate decisions.

  1. Automation of competitor activity detection

  2. More efficiently processing land lease records with NLP

  3. Utilization of drones to assist and enhance safety around field surveys

AI, ML and NLP are poised to change the energy industry in significant ways, and this is just the beginning. If you are thinking about incorporating Artificial Intelligence to your GIS or analysis practice, reach out to our team for a consultation.