SwRI will be exhibiting at the Natural Gas STAR & Methane Challenge Workshop, Booth No. not assigned.
Wednesday, Nov. 6
3:15 p.m. – 4:15 p.m.
“Airborne Deep Learning to Detect Methane Leaks” Heath Spidle
Smart Leak Detection/Methane technology, also known as SLED/M, was initially developed for stationary applications, such as fence-line monitoring of midstream facilities, we are currently advancing the technology to perform autonomously from drones for commercial aerial inspections. Conventional detection systems, designed to locate larger leaks, suffer from false positives and missed detections, which hamper effectiveness and utilization by industry. SwRI designed SLED/M to identify small methane leaks that typically go unnoticed along pipelines and storage facilities. By optimizing algorithms to reliably detect leaks under a variety of environmental conditions, SLED/M substantially reduces false positives. The system detects and pinpoints small methane leaks, known as fugitive emissions, by pairing passive optical sensing data with artificial intelligence algorithms and embedded edge computing.