The Role of Modeling Frameworks in Assessing New Methane Leak Detection Protocols

Methane emissions from the oil and gas industry threaten to erode the climate benefits of using natural gas over coal. With increased shale gas production, governments in the US and Canada have developed regulations to mitigate methane emissions by 40 - 45% by 2025. A major component of these regulations are periodic leak detection and repair (LDAR) surveys, typically using optical gas imaging (OGI) technologies. While OGI-based LDAR surveys have been implemented at oil and gas facilities, new technologies and sensor platforms like drones and satellites promise more cost-effective leak detection solutions. However, adopting these new technologies requires demonstrating that they can achieve 'equivalent' emissions reductions compared to current practices.

In this work, we explain how dynamic models such as the Fugitive Emissions Abatement Simulation Toolkit (FEAST) can be used to assess new technologies and platforms for their mitigation effectiveness. We show how technology parameters like minimum detection limits, quantification uncertainty, and the rate of false positives obtained from controlled-release tests conducted at METEC can be integrated into FEAST to evaluate potential emissions reductions using these new technologies. More importantly, by incorporating publicly-available methane emissions data from oil and gas facilities, we demonstrate how FEAST can be used to directly compare emissions reductions from OGI-based LDAR with alternative LDAR approaches. We will conclude with a discussion of the role of field tests and modeling in helping regulatory agencies embrace technological innovation into LDAR programs.