Source Localization and Bayesian leak magnitude inference of sparse wireless sensor data to detect fugitive methane leak

Abstract

Source localization and emission strength quantification is an ongoing challenge for distributed pollution sources. Here we outline a wireless sensor approach to localize all potential emission sources on an oil and gas well pad under well controlled experimental conditions. Using backtracking algorithms and time synchronized methane and wind measurements, sources are attributed to equipment on the well pad. After localizing the sources, we estimate source magnitude and uncertainty using a Bayesian inference method. The approach outlined in this work can identify and quantify leaks in the close proximity of the sources under dynamic plume dispersion taking into account the site layout, potential source locations and the characteristics of the sensor network. Localization of the system is within a meter from the emission location and the Bayesian approach yields rates that are within a factor 3 of the actual rate. Further, the actual rates are generally within the 95% confidence intervals for the prediction.