Cost-Effectiveness of Methane Mitigation Using New Technologies: A Case Study in the Denver-Julesburg Basin in Colorado

The Oil and Gas (O&G) industry is the largest source of anthropogenic methane emissions and represents a low-hanging opportunity for near-term greenhouse gas reductions. Recent advances in leak detection technologies could make regulatory leak detection and repair (LDAR) programs more cost-effective than current approaches. However, most of the new technologies have not been formally incorporated within state or federal LDAR policies. In this presentation, I will discuss the results of a recent case-study modeling upstream O&G facilities in the Denver-Julesburg (DJ) basin using the Fugitive Emissions Abatement Simulation Toolkit (FEAST). We modeled LDAR programs that deploy three new leak detection technologies including a drone-based sensor, a site-level aerial sensor, and an equipment-level aerial sensor. Emissions mitigation from these new LDAR programs are compared to conventional LDAR policies that use optical gas imaging (OGI) based surveys. We explore the role of LDAR program parameters such as survey frequency and tiered detection approaches in achieving equivalency with existing programs. The results of this study will provide critical insights on the deployment of new technologies as part of LDAR programs in the DJ basin. First, I will present the results for each LDAR program that incorporate new technologies and highlight the role of survey frequency on emissions mitigation. Second, I will discuss mitigation equivalence between the OGI-based LDAR program scenarios and the hybrid LDAR scenarios. Third, I will highlight the critical role of super-emitters in determining the efficacy of new technologies in mitigating methane emissions. Results from this modeling point to potential future areas of field research that will help better characterize O&G methane emissions.