Few industries present such diverse and difficult challenges as oil and gas. From operating in some of the world’s most hostile landscapes and oceans to precision engineering, sophisticated production chains, and complex data analysis; oil and gas companies face a unique range of demands in ensuring both the safety of their workers and productivity of their business.

It’s a range of challenges that’s well suited to the solutions provided by generative AI (GenAI). Although the impact of the technology will likely grow to touch every facet of this industry and others, there are several key areas where GenAI can make a difference now; namely improving the safety of workers, optimizing maintenance, and boosting productivity throughout organizations.

Like other industries, oil and gas companies have digitized a lot of their data in recent years. They’ve also been a leader in applying predictive analytics, for example to optimize the performance of equipment and to identify when it might be about to fail.

GenAI solutions have the potential to enhance and expand these capabilities through their ability to access a wider range of data, apply contextual reasoning, and operate through natural language queries.

Creating a safer workplace

The oil and gas industry is more dangerous than most. Employees are often operating in remote locations and extreme weather or working with explosive materials and heavy machinery.

The statistics speak for themselves. The US oil and gas industry reports over 11,000 severe injuries a year and a fatality rate that is seven times higher than other industries. It’s a high cost for affected workers and their families, as well as a big expense and risk for oil and gas companies. Oil and gas companies have an opportunity and obligation to ensure their associates and team members return home safely after every shift.

What can AI do to help? Like so many solutions today, it involves more data and better ways to understand that data. Most oil and gas companies have amassed a mountain of data about hazards, weather, accidents and injuries in the workplace.

That data can be leveraged to improve safety in three key ways.

Analyze information within an organization, industry, and elsewhere to identify patterns and anomalies that illustrate when and why accidents occur.
Interact with this data via a smooth natural language interface such as text, images, and voice to improve and accelerate how this data is analyzed.
Predict future vulnerabilities thanks to AI’s rapidly improving ability to collate vast amounts of information and suggest solutions.

GenAI solutions don’t only pull together all of this data involving a company’s specific safety concerns, they can also extend its analytical reach to more information sources such as evolving government regulations to ensure the analysis is always precise and relevant.

Workers might be able to have a personal AI assistant by their side to communicate risks in real time. A pipeline worker in Texas could be warned about an increased risk of a leak or explosion, for example.

A Cohere customer is already using this in practice: When a technician arrives at a chemical spill, they inform their AI assistant of their location and the identified chemical. The AI then provides precise next steps to resolve the issue safely.

Improving predictive maintenance

A few years ago, a leak from the Keystone Pipeline in Kansas spilled more than 500,000 gallons of oil into a local creek. The cause? A faulty weld.

Oil and gas companies understand more than most that a small mistake can result in a massive cost to both their bottom line, the environment, and their public reputation. It’s why predictive maintenance is so important, and it’s why GenAI can play a key role in improving how companies maintain their equipment now to avoid harmful mistakes in the future.

There are a growing number of ways across industries that GenAI can enhance predictive maintenance. For example, airlines are using it to analyze data involving flights, travel patterns and human behavior to unlock insights about wear and tear on their aircraft.

In the context of oil and gas, GenAI could improve on current systems by analyzing and integrating data from more sources in real-time. Traditional predictive maintenance systems rely on historical sensor data, such as vibration levels, temperature, and pressure, to predict potential future failures. GenAI can go further by integrating real-time adaptive data like environmental conditions such as the weather, and unstructured data such as maintenance logs to give a more holistic, accurate picture of equipment maintenance needs.

These capabilities will be further enhanced by personal AI assistants accompanying service technicians as they work.

Taking over tedious tasks

Sometimes, the most important benefits are also the most mundane. Effective maintenance is often plagued by the fact that forms aren’t user-friendly and can introduce errors. A conversational AI assistant can help. Instead of having to complete tedious forms, a worker can simply relate their findings to the AI assistant.

Or consider the humble drill bit. Crucial equipment like this consumes an untold number of personnel hours as it journeys from its origin in a purchase order through to manufacturing, delivery, inventory, use, repair, and replacement. Failure to bring it along on a trip to a remote work site results in costly downtime.

By predicting demand for drill bits based on factors such as project timelines, environmental conditions, and operational history, a GenAI solution can minimize the risk of shortages or overstocking.

By easing the burden of repetitive tasks and enabling professionals to get immediate insights from data, GenAI can enable workers to focus on higher-value activities that directly impact operations. This not only saves time but enhances productivity across key industry job roles.

Engineers can leverage it to analyze vast amounts of unstructured data, improving decision-making and optimizing drilling operations by predicting equipment failures and minimizing downtime.
Operations managers can use it for real-time scheduling and logistics optimization, ensuring efficient resource allocation and reducing operational slack in the supply chain.
Executives can harness AI insights for strategic portfolio management, identifying opportunities for divestitures or investments based on market trends and operational efficiencies.

Going forward, GenAI will provide a suite of powerful solutions for oil and gas companies, triggering improvements in safety, predictive maintenance, and the automation of repetitive tasks. As this technology continues to mature, it promises to deliver increased productivity and safer conditions to an industry that’s critical to the global economy.

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Alex Williams