Spatially Explicit Methane Emissions from Petroleum Production and the Natural Gas System in California

Abstract

We present a new, spatially resolved inventory of methane (CH4) emissions based on US-EPA emission factors and publically available activity data for 2010 California petroleum production and natural gas production, processing, transmission, and distribution. Compared to official California bottom-up inventories, our initial estimates are 3 to 7 times higher for the petroleum and natural gas production sectors but similar for the natural gas transmission and distribution sectors. Evidence from published "top-down" atmospheric measurement campaigns within Southern California supports our initial emission estimates from production and processing but indicates emission estimates from transmission and distribution are low by a factor of approximately 2. To provide emission maps with more accurate total emissions we scale the spatially resolved inventory by sector-specific results from a Southern California aircraft measurement campaign to all of California. Assuming uncertainties are determined by the uncertainties estimated in the top-down study, our estimated state total CH4 emissions are 541 ± 144 Gg yr(-1) (as compared with 210.7 Gg yr(-1) in California's current official inventory), where the majority of our reported uncertainty is derived from transmission and distribution. We note uncertainties relative to the mean for a given region are likely larger than that for the State total, emphasizing the need for additional measurements in undersampled regions.

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

“…State and national governments in the US use this strategy to construct official emission estimates (e.g., California Air Resources Board, 2015; US EPA, 2016c). A number of academic and government efforts have produced bottom-up CO 2 and CH 4 emission estimates at local-regional (e.g., Gately et al, 2013;Jeong et al, 2014;Lyon et al, 2015;California Air Resources Board, 2015), national (e.g., Pétron et al, 2008;Gurney et al, 2009;Gately et al, 2015;US EPA, 2013;Environment and Climate Change Canada, 2016;Maasakkers et al, 2016), and global scales (e.g., Rayner et al, 2010;Andres et al, 2011;Oda and Maksyutov, 2011;Olivier et al, 2014;EC JRC/PBL, 2016). In this section, we primarily discuss bottom-up data with an eye toward how this information can be combined with top-down strategies.…”

“…At the regional scale, Jeong et al (2014) and Lyon et al (2015) estimate oil and gas CH 4 emissions from California for 2010 and the Barnett Shale region for 2013, respectively. Both studies find emissions that greatly exceed the EPA's estimates.…”

Atmospheric characterization through fused mobile airborne and surface in situ surveys: methane emissions quantification from a producing oil field

“…For comparison, a recent bottom-up estimate of CH 4 emissions based on production data for the Kern Fields estimated 10-40 Gg CH 4 yr −1 (68 % confidence level), by combining oil and gas production data with US-EPA emissions factors for associated wells (Jeong et al, 2014). Other CH 4 sources are unlikely to confuse this interpretation as petroleum system emissions are ∼ 20 times larger than estimated nearby livestock and landfill CH 4 emissions of ∼ 2.3 and 1.4 Gg yr −1 , respectively (Calgem, 2014).…”

“…For comparison, a recent bottom-up estimate of CH 4 emissions from the Kern Fields estimated 25 ± 15 Gg CH 4 yr −1 by combining oil and gas production data with emissions factors for associated wells used by US-EPA (Jeong et al, 2014); i.e., 19 August 2015 CH 4 emissions were a third above inventories. The derived flux lies within the inventory uncertainty but is higher, consistent with a recent metastudy of field studies of FFI production emissions, which showed significant underestimation in the EPA budget (Brandt et al, 2014;Miller et al, 2013).…”

Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

“…Direct observations of fluxes are not feasible at these scales, and gaining an understanding of flux budgets and controlling processes at these scales therefore invariably depends on a process of either "upscaling" small-scale flux observations or "downscaling" large-scale information provided by atmospheric concentration measurements. Upscaling strategies range from the implementation of mechanistic models calibrated using plot-scale flux observations (e.g., Richardson et al, 2012;Schaefer et al, 2012), to the development of statistical or machine learning approaches for elucidating dominant patterns (e.g., Beer et al, 2010;Jung et al, 2011), and to the combination of fine-scale flux measurements with activity data (e.g., fuel consumption for anthropogenic emissions, or burnt area for fire emissions) as the basis of emissions inventories (e.g., van der Werf et al, 2006;Jeong et al, 2014;Lyon et al, 2015). Downscaling strategies, on the other hand, most typically involve the solution of an inverse problem to elucidate spatially and temporally resolved flux information from upwind and downwind observations of atmospheric greenhouse gas abundance (e.g., Enting et al, 2002).…”