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Artificial Intelligence Has Big Implications for the Oil and Gas Sector

What Does It Mean for the Industry’s Future?

Artificial intelligence (AI), once a seemingly far-off and futuristic endeavor, has touched nearly every part of modern-day living. From the cars we drive to the smartphones in our pockets, AI is the engine behind many of the technological advances we’ve witnessed in recent years.

AI has infiltrated almost every sector imaginable, oil and gas included. There’s no question about it: AI is here to stay, and the oil and gas sector has no choice but to turn and face it head-on. The question that remains is: What does the rise of AI mean for the industry’s future?

The Benefits of AI in the Oil and Gas Sector

AI shows remarkable promise for the future of oil and gas. Of course, it’s not a silver-bullet solution, but it certainly serves as a compelling response to some of the most pressing challenges the industry faces, especially in regard to bolstering safety and overall operating efficiency.

AI can be leveraged across the entire oil and gas lifecycle, from exploration and production to refining and distribution.

Exploration and Production

For years, exploration has involved putting boots on the ground to seek out promising surface signs of oil and gas. While this method can prove fruitful, oil and gas companies always risk incurring inefficiencies if they rely wholly on manual processes. Human error is unavoidable, but an unsuccessful exploration — like drilling a dry well — can cost oil and gas companies between $5 million and $20 million per exploration site .

Deploying autonomous, AI-powered robots during the exploration phase can reduce the risk of human error and optimize the efficiency of resource exploration and analysis. We can look to ExxonMobil as an example of this: The oil and gas giant recently launched a fleet of low-impact, AI-powered robots that are capable of analyzing seabed conditions at depths of up to 6,000 meters. The robots minimize exploration risk while reducing harm to marine life.

AI also plays a role in surface analysis and geological assessment. For example, the Wadia Institute of Himalayan Geology (WIHG) recently deployed an AI-based technique that analyzes data from seismic waves, enabling faster and more efficient discovery of hydrocarbons. Moreover, AI-powered drones equipped with image sensors can capture seismic images, while advanced image-processing algorithms extract valuable information to provide accurate, detailed insights into underground geological formations.

Refining and Distribution

In the refining and distribution phase, AI serves as a critical tool for reducing costs and optimizing operational efficiency.

AI-powered digital twins, for example, provide real-time visibility and flexibility that enhances efficiencies in refineries. A digital twin is a data-driven virtual replica of a physical asset or process used to simulate and optimize the performance of that asset or process in real time. In the oil and gas industry, digital twins are used to create virtual models of oil fields, refineries, pipelines, and other assets.

AI also empowers oil and gas companies with predictive maintenance capabilities. This allows managers to schedule maintenance effectively and proactively, reducing downtime and improving asset reliability. And by optimizing processes like equipment scheduling and fleet management, AI helps plant operators reduce costs, increase efficiency, and improve safety.

Safety and Compliance

AI is becoming a powerful tool for improving safety and compliance in the oil and gas industry, reducing the risk of accidents and regulatory compliance issues.

Defect detection using AI algorithms can identify potential equipment failures or malfunctions before they occur. This enables companies to address issues before they become major problems, saving time and money while keeping workers safe from harm. AI can also identify the root causes and drivers of unexpected machine downtime, providing insights into how to optimize operations and prevent downtime in the future. This can increase the reliability of critical equipment and improve safety by reducing the likelihood of sudden equipment failures.

Furthermore, AI-powered solutions can monitor work sites to ensure workers follow safety regulations throughout the day. AI algorithms can identify potential safety hazards and alert workers in real time by analyzing video feeds and sensor data.

Case Study: AI in Oil and Gas

Royal Dutch Shell has taken its digital transformation seriously, utilizing AI to optimize both its upstream and downstream operations. The Dutch oil giant is currently deploying over 100 AI applications into production every year, leveraging them to monitor equipment like pumps and valves, forecast equipment behavior, and equip service stations with cameras.

Over the years, Shell has implemented several other AI initiatives to transform its operations and maintain its competitive foothold. These initiatives include deploying reinforcement learning in its exploration and drilling program, the rollout of AI at public electric car charging stations, and installing computer vision-enabled cameras at service stations. By leveraging AI-based predictive maintenance, the company has been able to optimize its use of resources, reduce equipment and maintenance costs, minimize production interruptions, and avoid unplanned downtime.

Challenges and Limitations of AI in the Oil and Gas Sector

Adoption of AI in the oil and gas sector has been slower than in other industries. That’s not just due to the risks associated with investing in emerging technologies — it’s also because AI has only recently started gaining widespread acceptance in the industry. This means that there are not many experienced professionals who have the skills needed to use AI effectively in this sector. Additionally, high turnover rates in the industry—especially among personnel with highly-specialized skill sets, including AI expertise—have led to a shortage of employees with the background and training necessary for this kind of work. These challenges have made it more difficult for the oil and gas sector to leverage the full potential of AI, but efforts are being made to address them so that the industry can benefit from the many advantages AI can offer.

Conclusion: The Future of AI in the Oil and Gas Sector

AI has the potential to transform the oil and gas sector by enabling companies to reduce costs, increase operational efficiency, and double down on safety and compliance. And while the industry has been relatively slow in adopting AI compared to other sectors, companies like ExxonMobil and Shell are proving that widespread use of AI in oil and gas is possible.

To fully realize the potential of AI in the oil and gas sector, smaller companies will need to focus on developing the necessary skills and capabilities, and investing in technologies that can help them optimize operations and revenue. As the energy landscape continues to evolve, it’s clear that AI will play an increasingly important role in shaping the future of oil and gas.

About the Author

Bob Hooper is a senior industry consultant at Hexagon with a successful track record in the development and implementation of operational excellence, lean manufacturing and reliability programs within the oil and gas, chemicals and consumer products industries. His areas of expertise include program management and change management, and he specializes in helping customers understand the intricacies and applications of a variety of digital technologies.

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