At a Glance
Researchers at Colorado State University have developed novel, frequency-based, natural gas plume algorithms for optical gas imaging of emissions to eliminate human intervention. The method detects the high-frequency motion of the gas plume in a video stream. After background removal, the size of the gas plume can be quantified by thresholding the detected plume and measuring its size relative to the camera’s field of view. The resulting metric eliminates the need for human evaluation of video streams.
Recent growth in natural gas production in the United States has increased focus on reducing greenhouse gas emissions from the natural gas supply chain. Methane, the primary constituent of natural gas, is also a potent greenhouse gas. Optical gas imaging (OGI) is frequently used for emission detection in upstream and midstream sectors of the natural gas supply chain. Current OGI methods typically use mid-range infrared video cameras tuned to absorption lines of light hydrocarbons to make natural gas emissions visible to human operators. Prior studies of camera output have used human interpretation to determine if an emission is visible in the video stream, making it difficult to standardize measures of visibility between tests or to automate large test suites.
- Algorithms are not specific to any one camera brand or type
- Eliminates qualitative human variable
- Does not require the training sets for machine learning methods
- Further development for real-time detection of plumes
- Provides quantitative basis for evaluation of emissions
- Detecting emissions in oil and gas infrastructure
- Other industries that utilize OGI