A Mobile Sensing Approach for Regional Surveillance of Fugitive Methane Emissions in Oil and Gas Production

Abstract

This paper addresses the need for surveillance of fugitive methane emissions over broad geographical regions. Most existing techniques suffer from being either extensive (but qualitative) or quantitative (but intensive with poor scalability). A total of two novel advancements are made here. First, a recursive Bayesian method is presented for probabilistically characterizing fugitive point-sources from mobile sensor data. This approach is made possible by a new cross-plume integrated dispersion formulation that overcomes much of the need for time-averaging concentration data. The method is tested here against a limited data set of controlled methane release and shown to perform well. We then present an information-theoretic approach to plan the paths of the sensor-equipped vehicle, where the path is chosen so as to maximize expected reduction in integrated target source rate uncertainty in the region, subject to given starting and ending positions and prevailing meteorological conditions. The information-driven sensor path planning algorithm is tested and shown to provide robust results across a wide range of conditions. An overall system concept is presented for optionally piggybacking of these techniques onto normal industry maintenance operations using sensor-equipped work trucks.

Estimation of methane emissions from the U.S. ammonia fertilizer industry using a mobile sensing approach

“…The filter was later used to mask and adjust the raw mixing ratio data to remove the adverse effects caused by this issue. The background methane mixing ratio (c b ) was estimated as the 5 th percentile of the ranked time series of raw methane mixing ratios (c r ) (Albertson et al, 2016;Brantley et al, 2014), and the above-ambient mixing ratios c = c rc b . The estimated c b is very close to the ambient methane mixing ratios measured upwind of the plant, suggesting that the determination of c b is robust.…”

“…Using data collected during multiple sensor passes, a Bayesian approach was adopted to characterize methane emission from the ammonia fertilizer plants. This approach is fully described in (Albertson et al, 2016); here only a brief introduction is included. Following Bayes's rule, the posterior probability density function (PDF) of the emission rate Q [kg/h] is (Albertson et al, 2016;Yee, 2008;Yee, 2012):…”

“…Thus, a uniform distribution is adopted as a non-informative prior (Yee, 2007). After the first sensor pass (with a valid measurement of c y ), equation (1) is updated recursively such that P (Q|I) is replaced by the posterior PDF (P (Q|c y , I)) derived from the previous sensor pass (Albertson et al, 2016).…”

Quantifying uncertainties from mobile-laboratory-derived emissions of well pads using inverse Gaussian methods

“…Referenced study uncertainty range* Ground-based thermal imaging Gålfalk et al (2016) 3-15 % Aircraft remote sensing Kuai et al (2016), Frankenberg et al (2016), Thorpe et al (2016) 5-20 % Satellite remote sensing Kort et al (2014) 15 % Chamber sampling Allen et al (2013Allen et al ( , 2014, Kang et al (2014) 20-30 % Ground-based tracer correlation Lamb et al (2015), Roscioli et al (2015), , Zimmerle et al (2015), Omara et al (2016) 20-50 % Aircraft/UAV mass balance Karion et al (2013Karion et al ( , 2015, Peischl et al (2013Peischl et al ( , 2015Peischl et al ( , 2016, Caulton et al (2014), Pétron et al (2014), Lavoie et al (2015), Nathan et al (2015) 20-75 % Ground-based stationary dispersion Brantley et al (2014), Foster-Wittig et al (2015 25-60 % Tall-tower monitoring Pétron et al (2012) 50-100 % Ground-based mobile dispersion Lan et al (2015), Rella et al (2015), Yacovitch et al (2015) 50-350 % * Uncertainty range reflects author-reported uncertainty on emission numbers, not necessarily measurement uncertainty. Some authors specify a 95 % confidence interval, others use 1 or 2 standard deviations and others compute upper and lower bounds.…”

Event clustering & event series characterization on expected frequency

“…Applications that have to characterize peaks of measurements can benefit. One of them is the problem of geo-locating leaks through a network of detector sensors [18] such as in the field of industrial pollution detection [19].…”