Bayesian estimation of methane emission rates from a single high-frequency gas sensor
In onshore oil and gas exploration and production, new tools are needed to support environmentally responsible development of these key national energy assets. Concerns for air pollution emissions from this sector include the unintentional release of product-related fugitive emissions of methane. Development of automated mobile sensor technologies is an emerging environmental monitoring area that may provide useful tools to industry and regulators for cost-effective identification and repair of fugitive emissions. The development and testing of a Bayesian analysis technique that best defines the posterior probability density functions of the fugitive emission rates from single-point high-frequency methane time series is presented. This is an abstract of a paper presented at the AWMA's 107th Annual Conference & Exhibition (Long Beach, CA 6/24-27/2014).
