⚙️ Beyond Neural Networks: Smarter AI for Oil & Gas Operations

Today, many oil and gas initiatives are focused on AI drone automation built on a single class of AI deep neural networks.

While powerful, these models come with a cost—including rising GPU and cloud expenses, limited access to AI talent, and massive data requirements.  Making it expensive and slow to deploy AI especially in constrained, high-risk environments like offshore drilling or pipeline monitoring.

🧠 A New Paradigm: Scenario-Based Intelligence (SBI)

An alternative AI framework built for physical-world complexity

Scenario-Based Intelligence (SBI) offers a smarter, leaner alternative. It’s a paradigm engineered specifically for real-time, edge, and safety-critical environments—making it highly relevant for the constraint environment the oil and gas companies work in.

What Makes SBI Different?

Unlike traditional AI that relies on static predictions, SBI combines temporal memory and contextual reasoning. It doesn’t just process isolated data points—it learns from the sequence and significance of events, similar to how a seasoned operator can recognize patterns from experience.

Instead of needing huge amounts of labeled examples, SBI:

• Uses a fraction of the data traditional models require—making it ideal for environments where labeled datasets are scarce.

• Adapts to new situations on the fly

• Offers explainable, traceable decisions essential for regulatory and operational integrity

Imagine an AI system in your refinery that doesn’t just react—but understands why it's making a process adjustment based on the chain of preceding conditions. SBI combines the explainability of expert systems with the adaptability of modern AI.

🚁 From Drones to Downstream: SBI in Action

The AI in drones market is expected to reach $206.9B by 2033, up from $2.5B in 2023, with a 32.4% CAGR. In oil and gas, drones are already deployed with Autonomous drilling and robotics, especially in offshore environments. 

In 2023, over 120 rigs used AI-guided robotic arms and autonomous inspection drones for tasks like pipeline scanning, underwater weld analysis, and platform surveillance. Cutting inspection cycle times by nearly 40%. (Artificial Intelligence in Oil and Gas Market Size).

The problem with current AI automation of drones is that: 

• Pricey & energy hungry GPUs make each robot cost more.

• A lot of cloud computing is required. This brings latency, slowing or blocking responses in network-denied zones completely. 

• Work in a risky environment requires transparency in decision making. 

If you're shaping the future of the oil and gas market, you don’t need another power-hungry, AI model. You need intelligence that aligns with your robot's form factor—and your business objectives.

When ready to equip your robots with the next generation of intelligent automation Contact us on contact@mentalengines.ai and explore how we can revolutionize your robots capabilities  📧.