Performance evaluation of multi-source methane emission quantification models using fixed-point continuous monitoring systems

Abstract

Quantifying methane emissions from oil and gas facilities is crucial for emissions management and accurate facility-level greenhouse gas (GHG) inventory development. This paper evaluates the performance of several multi-source methane emission quantification models using the data collected by fixed-point continuous monitoring systems as part of a controlled-release experiment. Two dispersion modeling approaches (Gaussian plume, Gaussian puff) and two inversion frameworks (least-squares optimization and Markov chain Monte Carlo) are applied to the measurement data. In addition, a subset of experiments are selected to showcase the application of computational fluid dynamics (CFD) informed calculations for direct solution of the advection–diffusion equation. This solution utilizes a three-dimensional wind field informed by solving the momentum equation with the appropriate external forcing to match on-site wind measurements. Results show that the Puff model, driven by high-frequency wind data, significantly improves localization and reduces bias and error variance compared to the Plume model. The Markov chain Monte Carlo (MCMC)-based inversion framework further enhances accuracy over least-squares fitting, with the Puff MCMC approach showing the best performance. The study highlights the importance of long-term integration for accurate total mass emission estimates and the detection of anomalous emission patterns. The findings of this study can help improve emissions management strategies, aid in facility-level emissions risk assessment, and enhance the accuracy of greenhouse gas inventories.