Preventing pipeline damage, which often leads to leaks and explosions, is a top priority for energy operators, yet traditional monitoring methods often fall short in identifying risks early enough. A major Spanish energy operator, overseeing a large pipeline network, faced limitations with conventional approaches such as helicopter inspections, which frequently resulted in false alarms and delayed detection of real threats. Additionally, relying on several separate monitoring tools complicated workflows and increased inefficiencies.

In response, the operator adopted a satellite-based monitoring approach that integrates high-resolution imagery with AI and machine learning. This method allowed for more accurate identification of third-party interference (TPI) and other potential pipeline threats, with the goal of developing a more predictive model for damage prevention.

The outcomes were compelling: 73% of previously missed threats were successfully detected, and the accuracy of threat predictions increased by 80% after seven monitoring cycles. By reducing the reliance on multiple software tools, the system also cut down manual tasks and improved operational efficiency, with a four-hour weekly reduction in workload.

This session will elaborate on the case study and its outcomes, detailing how these advancements can benefit pipeline operators and enhance their monitoring practices. Attendees will gain valuable insights into the practical applications of satellite technology and AI, and a live demonstration will showcase how these tools can create a more predictive and proactive approach to preventing leaks, explosions, and other risks in pipeline networks.