We present single-blind test results for quantification of very high emission rates using Bridger Photonics, Inc.’s (Bridger’s) airborne Gas Mapping LiDAR technology. Flowmeter measurements from a controlled release are compared with emission rate quantification estimates from Gas Mapping LiDAR to determine Bridger’s measurement bias and uncertainty.
Remotely quantifying the emissions rate of methane gas (the primary constituent in natural gas) is becoming increasingly important across the oil and gas industry. Accurate aggregate emissions rates are critical as inputs to predictive climate models and for policy regarding emissions reduction targets and regulations. Many companies in the oil and gas industry have set voluntary methane emissions reduction goals and some have even tied executive compensation to the achievement of those goals.
To address these emerging applications, Bridger set out to assess the methane emissions rate quantification capabilities of Gas Mapping LiDAR technology, specifically for very high emissions rates.
Since a single very large emitter may constitute a reasonable fraction of an aggregate emission or inventory, it is important that the accuracy with which very high emission rates can be quantified by Gas Mapping LiDAR is well understood. A large quantification error on such a large emitter can lead to relatively high uncertainty in the aggregate inventory and thus must be considered in developing a plan for achieving the emissions reduction goal for a particular application. To get similar averaging for statistically infrequent very large emitters it may be desired to repeat the measurement multiple times to reduce the random error and thereby approach the measurement bias.
A few highlights and takeaways from the testing included:
To see the methods, full results, and outcomes of this study, download our whitepaper now.
*This may be optimistic to expect consistently as explained in the paper… single-digit percent is more reasonable to manage expectations. Regardless, the results provide strong support for the use of GML for quantifying aggregate emissions.