Optimal Sensor Path Planning for the Detection and Quantification of Fugitive Gas Emissions
Oil and natural gas are valuable energy sources; however, their production is a leading source of atmospheric methane, a harmful greenhouse gas. The majority of the methane emitted by the oil and natural gas industry occurs during the field production stages of the natural gas and oil extraction process. This is primarily attributed to uncontrolled, fugitive emissions. Determining the sources of fugitive emissions is critical in reducing the impact of the natural gas industry on the environment. The proposed research will address the problem of detecting methane emissions from distributed natural gas and oil field production sites. Through the work of the Environmental Protection Agency (EPA) Geospatial Measurement of Air Pollution (GMAP) program, methane concentrations along public roadways and measurements of atmospheric conditions have been obtained by mobile sampling vehicles. Using the collected data, the researcher will help to develop an inverse dispersion method to identify the sources of the fugitive emissions. Furthermore, this project will investigate optimal path planning for the mobile sampling vehicle, in order to maximize the information potential – i.e. minimize uncertainty in source strength estimates, while minimizing the operational costs of the mobile sensor. The methodology developed will integrate environmental forecasts, modeling of environmental processes, and mobile sensor navigation strategies, in order to determine a strategy for the cost effective inspection of natural gas an oil field production sites for the generation of fugitive emissions statistics by way of concentration and atmospheric data obtained on public roadways.