SBIR Phase II: Bridging Fugitive Emission Monitoring Gaps via Artificial Intelligence and Hybrid Sensors

Award ID: 2537735

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project would be in empowering oil & gas producers to cost-effectively detect emissions from their facilities early through sub-weekly, high-resolution AI-powered satellite scans integrated with continuous 24/7 onsite monitoring. This hybrid approach delivers comprehensive leak detection, facilitates swift mitigation, and generates credible certification data enabling premium pricing for produced natural gas. The primary, high-risk technical innovation of this work is a deep learning model designed to automatically detect and quantify asset-level specific emissions in freely available, 30-m-resolution satellite imagery not originally designed for detecting those emissions (Landsat, Sentinel-2). The core objective is to integrate training imagery coincident with known emissions below the theoretical detection threshold of these satellites when using traditional methods (200-500 kg/hr under ideal conditions). With proprietary, high-quality training data, the model may achieve detection thresholds similar to commercial satellites (
NSF Program Director: Rajesh Mehta