This report outlines the findings and results from Round 2 (R2) of the field-testing campaign to simulate real-world environmental conditions under a controlled setting at Colorado State University’s Methane Emissions Technology Evaluation Center (METEC). We present our progress in developing, testing, and implementing methods to quantify methane emissions from oil and gas facilities using our innovative analytics platform. This platform integrates continuously monitored data from the Canary X detectors, meteorological conditions, and cloud analytics to detect and quantify methane emissions from remote locations.
We performed continual testing of methane emissions throughout the entirety of three days to investigate the diurnal effects on our quantification methods. The design of experiments included a total of 45 test conditions (experiments) that included programmed methane releases from multiple sources at a natural gas site, including gas processing units, well heads, and storage tank batteries. A total of eight CANARY sensors networks were deployed at the Fenceline of the 200 ft x 280 ft site with a detector to source distance ranging from 69 to 230 ft. The duration of each test lasted 60 minutes, followed by a 15-minute remission period when no methane was released. The goal for each test was to establish a baseline for the following test and so on. Each leak rate was repeated three times with a total of 45 experiments to examine if our quantification models could provide reproducible or consistent results. The controlled methane leaks ranged from low to high release rates between 0.05 g/s (~10 scfh) all the way up to 0.84 g/s (~160 scfh) to represent average well pad emissions on natural gas sites. The wide range of methane releases offered a great way to test the robustness of our quantification models.
As part of the quantification methods, we have developed and examined two models (Model N and Model S) for quantification to thoroughly investigate the problem and employ the best evaluation methods. The findings indicate that the quantification methods are robust under variable weather conditions when the average wind speed ranges from 0.5 m/s to 6 m/s at the site footprint and for different sensor configurations. Furthermore, both quantification methods demonstrate that they can detect methane leaks with a total site emission prediction error ranging from -16% to 3% at the mentioned release rates. Total predicted site emission is the cumulative predicted emission rates of each experiment over the total test period of three days. The true total site emission (cumulative over the full 3-day test period) was 50.22 kg of methane. The predicted values were 58.1 kg of methane released for Model S and 48.74 kg of methane released for Model N. The goal is to converge to a single analytics platform that will integrate the best features from each method.
About Project Canary
Project Canary is an independent certification organization that measures, tracks, and delivers trusted ESG data across the energy value chain. They are the leaders in the rating and certification of responsible energy operating practices and provide science and technology-backed emission profiles via continuous monitoring hardware synced with a real-time dashboard. Project Canary Upstream (TrustWell) Certifications, Midstream Certifications, and Canary Continuous Monitoring help identify the most responsible energy supply chain operators. Formed as a Public Benefit Corporation, Project Canary’s team of scientists, engineers, and seasoned industry operators have earned recognition for their uncompromising standards, including being named “Best for the World 2021” B Corp. projectcanary.com