Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach

Abstract

An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from the upstream oil and gas sector in a variety of contexts, from component-level measurements to yearly industry-wide inventories. One possibility is to derive an uncertainty estimate from the physical model that connects the measurement data to the emission estimates directly, but this information is often proprietary and thus unavailable to end users. Instead, we provide a method to develop probability distributions of measurements given a true emission rate empirically using controlled release data. This method is completely technology-agnostic, and provides a route to summarise uncertainty without the need to release proprietary modelling or data. To demonstrate the wide applicability of the method, we introduce an algorithm that can be used to synthesize the uncertainty model and measurement-based surveys to produce an uncertainty range for new measurements in the field.