Intelecy no-code industrial AI

Oil & Gas

Engineer-driven AI for Oil & Gas Operations

Oil and gas operations run at the edge of safety, reliability, and efficiency. Small deviations in process conditions can escalate quickly, and the window to act is narrow. Every minute of delayed detection translates directly into production loss, risk to asset integrity or energy waste.

Intelecy helps oil and gas operators turn existing operational data into practical AI-driven insights that support safer, more efficient, and more reliable operations, without disrupting established systems or workflows.

In oil and gas, availability is everything. Before optimization comes reliability, assets have to run. Intelecy helps operations teams reduce the risk of unexpected failures, avoid unplanned shutdowns, and maintain maximum availability. When a facility is running reliably, optimization follows naturally.

Whether the goal is improving compressor reliability, reducing energy consumption, or moving toward more autonomous operations, Intelecy supports a stepwise approach, starting with decision support and scaling toward closed-loop optimization as confidence and value are established. At one of our oil and gas customers, the identified value potential across key use cases exceeds USD 10 million by 2030, through avoided downtime, reduced process trips, and energy savings.

I was able to set up my first machine learning model with no training from anyone in Intelecy. That’s the power of the platform.
Alfred Nerland
Reliability & Digital Engineer at Shell
shell-logo
Oil & Gas use cases
AdobeStock_8699563 - Chemicals

USE CASE

Predicting compressor dry seal gas failures 

Subtle pressure changes can signal compressor seal degradation long before failure, if you know what to look for.

AI models trained on historical failure data give engineers the early warning they need.

Engineer on site

USE CASE

Early gearbox fault detection and condition monitoring 
 
Early gearbox faults are easily masked by normal load and vibration changes.

Anomaly detection models trained on historical data help teams spot developing issues before they become unplanned downtime.  

AdobeStock_61088469 - petrochemical

USE CASE

Energy optimization of compressor cooling 

Compressor cooling involves constant trade-offs between throughput, efficiency, and energy use.

Intelecy's Data Explorer helps operators identify and stay within the optimal operating range.

Stay updated

Explore our latest articles, news, and insights to stay informed about industry developments and trends.

Blog

What Does 'Good Data' Mean for Machine Learning in Industry?

When implementing machine learning solutions for industrial manufacturing, the quality of...

How Many Successful Pilots Does it Take to Change Nothing?

A successful pilot often feels like real progress. But that feeling can be misleading. A...

Bridging the Workforce Readiness Gap

This is Part 2 of our three-part series on unlocking real, scalable impact with...

Try Intelecy for free