AI in Oil and Gas: Practical Applications, Challenges, and the Path to Smarter Operations

How AI is Shaping the Oil and Natural Gas Industry

AI headlines often hype superintelligence, but in oil and gas, the impact is more grounded. The current AI applications focus on streamlining operations, cutting costs, and improving safety rather than inventing new exploration methods or expanding markets.

This creates a paradox: AI is seen as a tool for boosting profits, yet most deployments aim at reducing expenses. While cost-cutting improves margins, it doesn't guarantee sustained growth. Here’s a clear look at how AI affects this industry now and what lies ahead.

Where Is AI Making a Practical Difference Today?

AI is enhancing engineering software in areas like predictive maintenance, process optimization, and resource management. These improvements lower operating costs, enhance safety, and reduce risk. They also increase confidence in decision-making, helping teams act faster and smarter.

Future AI Impact Areas

• Materials Science: Predicting molecular interactions in materials remains challenging, leading to costly trial-and-error. AI breakthroughs, like DeepMind’s protein folding, hint at faster development of materials that endure extreme conditions—critical for oil and gas infrastructure.
• Text-to-Design Applications: AI can soon transform short text inputs into 3D engineering designs, accelerating the exploration of multiple alternatives. This applies to valves, pressure vessels, and gas processing plants, making design work more efficient.
• AI Code Generation: Automated code generation will evolve to build entire systems, transforming software development from manual to automated. This will impact SCADA systems, process controls, and autonomous vehicles in oil sands operations.

How AI Is Changing Productivity, Profitability, and Risk Management

AI already boosts productivity in research and document writing. It uncovers cost-saving opportunities in capital projects and operations, especially in fabrication and maintenance, improving profitability.

However, AI also introduces new risks. For example, AI bots in finance can collude, manipulate prices, and sideline humans—raising regulatory concerns. Oil and gas companies should conduct thorough risk assessments before deploying AI applications.

Challenges Slowing AI Adoption

Many internal AI projects fail due to flawed integration, not poor models. Studies show external vendors specializing in AI deliver twice the success compared to in-house pilots.

Data quality is a major hurdle. Cleaning and preparing vast datasets used in oil and gas is costly and time-consuming, more so than in many other industries.

Ethical concerns also arise, including:

• Misinterpretation of scientific concepts leading to wrong recommendations
• AI hallucinations producing incorrect or misleading answers
• Baked-in biases related to race and gender

Addressing these requires active involvement from management, engineering, and IT leaders.

Key Steps for Successful AI Adoption

• Define clear business goals: Focus AI projects on specific problems or opportunities, not general experimentation.
• Measure what matters: Use data-driven metrics rather than subjective impressions.
• Design for data-driven decisions: AI should support informed choices, not replace human judgment entirely.
• Foster human-AI collaboration: Combine AI insights with human expertise for best outcomes.
• Evaluate business viability: Ask “Should we?” before “Can we?” – prioritize business cases over technical capability alone.

Governance Priorities for Oil and Gas Leaders

Boards and executives must guide responsible AI use with policies and oversight. Important focus areas include:

• AI Acceptable Usage Policy: Clearly define permissible employee uses of AI tools.
• AI Risk Management: Adopt established risk frameworks, like the MIT AI risk framework.
• Mitigating AI Hallucinations: Set expectations for thorough testing to prevent false outputs.
• Project Best Practices: Treat AI initiatives with the same rigor as other projects.
• Cybersecurity of AI Applications: Ensure AI tools include strong defenses against cyber threats.
• Using AI for Cyber Defense: Counter AI-driven attacks by maintaining advanced cybersecurity measures.

What’s Next for AI Development?

Currently, AI is a tool that waits for human input. The next stage, Artificial General Intelligence (AGI), will advance AI’s reasoning to human-like levels, potentially enabling it to propose better questions and support strategic decisions.

Agentic AI, which acts autonomously and takes initiative, represents the future. It shifts AI from just answering questions to orchestrating actions. Despite these advances, human insight will remain essential in framing problems and interpreting outcomes.

As AI evolves, oil and gas leaders should stay informed and prepared to integrate AI thoughtfully, ensuring it adds real value while managing risks effectively.