Variability in Spatially and Temporally Resolved Emissions and

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Variability in Spatially and Temporally Resolved Emissions and Hydrocarbon Source Fingerprints for Oil and Gas Sources in Shale Gas Production Regions David Thomas Allen, Felipe J. Cardoso Saldaña, and Yosuke Kimura Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02202 • Publication Date (Web): 14 Aug 2017 Downloaded from http://pubs.acs.org on August 16, 2017

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Environmental Science & Technology

Variability in Spatially and Temporally Resolved Emissions and Hydrocarbon Source Fingerprints for Oil and Gas Sources in Shale Gas Production Regions David T. Allen*, Felipe J. Cardoso-Saldaña, Yosuke Kimura Center for Energy and Environmental Resources, University of Texas at Austin, 10100 Burnet Road, Austin, TX 78758, USA

*Corresponding author: email: [email protected] ; tel.: 512-475-7842 Keywords: Natural gas, oil and gas sector emissions, methane, ethane Abstract

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A gridded inventory for emissions of methane, ethane, propane and butanes, from oil and gas

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sources in the Barnett Shale production region, has been developed. This inventory extends

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previous spatially resolved inventories of emissions by characterizing the overall variability in

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emission magnitudes, and the composition of emissions, at an hourly time resolution. The

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inventory is divided into continuous and intermittent emission sources. Sources are defined as

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continuous if hourly averaged emissions are greater than zero in every hour, otherwise they are

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classified as intermittent. In the Barnett Shale, intermittent sources accounted for 13-30% of the

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mean emissions for methane and 10-32% for ethane, leading to spatial and temporal variability

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in the location of hourly emissions. The combined variability due to intermittent sources and

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variability in emission factors can lead to wide confidence intervals in the magnitude and

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composition of time and location specific emission inventories, therefore, including temporal and

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spatial variability in emission inventories is important when reconciling inventories and

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observations. Comparisons of individual aircraft measurement flights conducted in the Barnett

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Shale region vs. the estimated emission rates for each flight, from the emission inventory,

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indicate agreement within the expected variability of the emission inventory for all flights for

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methane, and for all but one flight for ethane.

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Introduction

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Atmospheric emissions from oil and gas sources in the United States are reported through a

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variety of inventories. At the national scale, emissions of Volatile Organic Compounds (VOCs),

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nitrogen oxides (NOx) and other criteria air pollutants are reported through the US

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Environmental Protection Agency’s (US EPA) National Emission Inventory.1 Emissions of

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greenhouse gases from oil and gas sources are reported through the US EPA’s Greenhouse Gas

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Inventory2 (US EPA, 2016b) and the US EPA’s Greenhouse Gas Reporting Program (US EPA

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GHGRP).3 A number of state and region specific inventories are also available. For example,

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the States of Pennsylvania and California assemble state-wide greenhouse gas emission

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inventories;4,5 the State of Texas prepared the Barnett Shale Special Inventory for emissions of

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VOCs and NOx from oil and gas sources in the Barnett Shale oil and gas production region in

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North Central Texas.6 These emission inventories are generally reported on an annual basis and

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with varying degrees of spatial resolution. For example, the Greenhouse Gas Reporting Program

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of the US EPA reports location information for individual facilities that are sources of

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greenhouse gas emissions; the US EPA’s Greenhouse Gas Inventory reports emissions at a

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national scale, and for the oil and gas sector provides data on broad oil and gas production

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regions. Recently, the 2012 US EPA Greenhouse Gas Inventory has been reported for a 0.1o by

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0.1o spatial grid with a monthly temporal resolution.7 Current emission inventories are also

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generally reported with only limited information about the molecular composition of the

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emissions. For example, greenhouse gas emission inventories typically only provide information

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on methane, carbon dioxide and nitrous oxide emissions; inventories reporting criteria air

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pollutant emissions typically report total emissions of VOCs and NOx; users of the inventories

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supply source composition profiles to estimate the molecular composition of emissions.8

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Depending on how the emission inventories are to be used, the spatial, temporal and molecular

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resolutions of current inventories may be adequate or may need refinement. For example, if the

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objective is to determine annual emissions of greenhouse gases to assess long term trends in

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national emission patterns, then the spatial, temporal and molecular resolution provided in

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current inventories are generally adequate. In contrast, if inventories are being used to reconcile

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emission estimates with ambient observations that have time resolutions that are daily averages,

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hourly averages, or of even shorter duration, current inventories may be inadequate. A challenge 2


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associated with performing short duration reconciliations between observations and inventories

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is the intermittent nature of some oil and gas emission sources.

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demonstrated that a significant fraction of the emissions from the oil and gas sector originate

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from a relatively small fraction of the sources, and that the specific sources responsible for the

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emissions change over time. For example, in helicopter overflights of approximately 8,200 sites

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throughout the United States, 4% of the sites had individual sources with emissions estimated to

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be greater than 1-3 grams/second (g/s), and detectable by infrared camera. 9 These sites with

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high emission have been labelled as ‘super-emitters’10 however, this term is not universally

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accepted11 and has been used in many different contexts. In this work, the upper portion of the

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emission rate frequency distribution will be referred to as high-emitters. The frequency of high-

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emitting sites observed in helicopter surveys ranged from less than 1% in the Powder River

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Basin of Wyoming to 14% in the Bakken Shale production region in North Dakota.9 These high-

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emitting facilities can account for a large fraction of both instantaneous and annual average

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emissions in oil and gas production regions.10,12 Some of these snapshots of high emitting sites

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are capturing activities that are intermittent (e.g., a tank flashing event or a liquid unloading), and

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therefore would be expected to change location, depending on the specific instant that is captured

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in the monitoring. Recent analyses of data from the Barnett Shale suggest that some high

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emission rates may also be due to unplanned events, which might also be expected to be

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intermittent and variable in location. Because some intermittent events (e.g., a condensate tank

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flash event) are expected to have different compositions than other intermittent events (e.g., a

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liquid unloading), the molecular nature of oil and gas emissions also varies over time scales of

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hours to days.

Recent studies have

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The focus in this work will be on developing a regional emission inventory for oil and gas

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sources with a fine level of spatial, temporal and molecular resolution. Previous analyses have

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provided detailed spatial resolutions of emissions.7,13 This work will extend those analyses by

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characterizing the temporal and molecular variability in emissions at a fine spatial resolution.

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The Barnett Shale oil and gas production region in North Central Texas will be used as a case

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study in this work, because methane and ethane measurements and methane emission factor and

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activity data available for the Barnett Shale are more detailed than for many other regions in the

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United States. Nevertheless, the methods developed in this work could be used to create similar

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emission inventories for many other oil and gas production regions.

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Methods

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A gridded emission inventory (4 km by 4 km grid cells) for the Barnett Shale oil and gas

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production region in North Central Texas, with hourly time resolution, was developed. The

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emissions are estimated for typical hours during the 2013 calendar year. The Barnett Shale is

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one of the largest natural gas production regions in the United States. More than 20,000 wells

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produced approximately 5.4 billion standard cubic feet of natural gas per day in 2013.14 In this

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work, emissions were estimated for methane, ethane, propane and butanes; additional molecular

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species could be estimated through relatively simple adaptations of the methods, a brief

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discussion of this adaptation can be found in Supporting Information. The following sections

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describe how data were assembled for molecular compositions, and for characterizing the spatial

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and temporal distributions of the emissions.

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Molecular composition of oil and gas emission sources

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Figure 1 provides a conceptual diagram of an oil and gas production site. Produced fluids exit

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the well head and flow through a valve which reduces the pressure of the fluids to levels that

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range from more than 200 pounds per square inch absolute (psia) to less than a hundred psia.

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The fluids, at reduced pressure, are sent to a separator, which is designed to have enough

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retention time to achieve thermodynamic equilibrium and to separate condensate, water and gas

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phases.15,16

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storage tanks, where some hydrocarbons, which dissolved in the water or condensate at separator

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conditions, flash and exit the tank. The gas products exiting the separator as the overhead stream

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are typically directed to a sales line, sometimes after compression. Some of the separator

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overhead may be used on-site to fuel compressors or power pneumatic devices. The bulk of the

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separator overhead goes from the production site to gathering facilities, which aggregate

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produced gas from multiple production sites and which may remove water and/or provide

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compression. Produced gas from multiple gathering sites is sent to a gas processing plant, which

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produces pipeline quality gas. If the produced gas contains significant quantities of ethane and

Liquid products from the separator are generally sent to atmospheric pressure

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heavier hydrocarbons, natural gas liquid products (ethane, propane, butanes) may also be

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removed from the produced gas at the gas processing plant.

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This general flow pattern leads to five major compositions of emissions as gas moves from the

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well head to transmission pipelines. These categories of emission compositions will be referred

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to as separator feed, separator overhead (produced gas), condensate tank flash, water tank flash,

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and pipeline gas. In the Barnett Shale, and in some other production regions, data on the

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composition of separator overhead streams (produced gas) are available. In the Barnett Shale,

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these molecular compositions of produced gas (separator overhead) are available at the county

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level and report concentrations of individual molecular species up to a carbon number of seven.17

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Higher molecular weight species are characterized as a C8+ fraction. The compositions of other

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emitted gas streams (separator feed, condensate tank flash and water tank flash) can be

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calculated using thermodynamic models, the separator overhead composition, the API gravity of

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condensate liquids, separator operating conditions and product flow rates. The details of these

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calculations are provided in Supporting Information. Key features of the calculations are that,

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for light alkanes, (methane, ethane, propane and butanes) the ratios of C2-C4 alkanes to methane

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are predicted to be virtually identical for separator feed and separator overhead; however, the

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compositions of water and condensate tank emissions, and pipeline gas, are predicted to be

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substantially different than the separator overhead. Ethane to methane ratios in condensate tank

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flash emissions were typically 4-7 times higher than the ratios in separator overhead. Emission

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compositions for separator feed, separator overhead (produced gas), condensate tank flash, water

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tank flash, and pipeline gas were calculated for wet and dry gas production activities in each

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county in the Barnett Shale. Detailed data are provided in Supporting Information.

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Spatially resolved emissions by source category

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Emissions from individual sites in a production region were estimated using a Monte Carlo

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simulation that aggregates component emissions, analogous to the aggregation of components

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and the use of component specific distributions of emission factors developed by Zavala-Araiza,

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et al.10.12.

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inventories and by Lyon, et al.,13 because statistical distributions of emission factor values are

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employed, rather than average values. Since one of the primary goals of this work was to

This approach was used, rather than the approach used in national emission

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characterize variability in emissions in a production region, representing the variability in

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emission factors was essential. Each Monte Carlo simulation estimated 1,000 hourly instances

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of a gridded inventory, with selection of emission factors and locations for episodic emissions

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that varied from simulation to simulation. These 1,000 hourly instances represent the variability

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that might be expected over 1,000 hours of operation (approximately a month). Ten Monte

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Carlo simulations were run, resulting in 10,000 hourly instances to characterize an approximate

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measure of annual variability.

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The original method developed by Zavala-Araiza et al. estimated methane emissions for the

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17,400 production sites and 25,700 wells in the Barnett Shale production region.12 This work

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adds pre-production and mid-stream emissions to the estimates and estimates emissions of

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multiple light alkane species, using the emission composition profiles described in the previous

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section.

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Produced gas, water and condensate flowrates for each producing well in the Barnett were

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obtained from DI Desktop18, a database that aggregates and quality assures production data

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reported by the Texas Railroad Commission. Individual well data were clustered into sites by

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grouping wells that had reported locations within 100 m of each other into multi-well sites, as

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described in Zavala-Araiza et al.12. For each site, the number of wells, well age, oil (condensate)

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production, water production, and natural gas production are known.

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identified 17,400 sites with 25,700 wells (mean of 1.5 wells/site; median = 1 well/site; range of

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1-22 wells/site).12

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although not all well sites had all types of emissions. The eight categories of sources are

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chemical injection pumps, equipment leaks, compression systems, condensate tank flashing,

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water tank flashing, liquid unloading, pneumatic controllers, and dehydrators. Methods for

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estimating emissions and source profiles for each of these sources of emissions are described

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below, along with methods used for estimating mid-stream emissions,

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emissions and high-emitting sources.

Zavala-Araiza et al.

Eight types of routine emissions from producing well sites were identified,

well completion

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Chemical Injection Pumps Zavala-Araiza et al.12 used data from the Greenhouse Gas Reporting

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Program (GHGRP)3 to develop equipment counts for pneumatic chemical injection pumps. For 6


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the Barnett Shale, an average of 0.54 chemical injection pumps per well were reported for 2013,

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so the 25,700 wells in the region were estimated to have 13,900 gas driven chemical injection

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pumps. Locations for the pumps were randomly selected from the 17,400 well sites.

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was selected, all the wells on that site were assigned pumps. Emissions per pump were assigned

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based on data reported by Allen et al.19 for 62 chemical injection pumps. The average emission

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factor is 0.22 kg CH4/h per pump (range: 0.002-2.3 kg CH4/h). Emissions for each pump in the

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Barnett Shale were assigned by sampling from the distribution of measured emission rates.

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Emissions were assumed to be continuous for the purpose of constructing an hourly inventory

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gridded at a 4 km length scale. The emissions for non-methane species were calculated based on

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methane emissions and composition ratios (e.g., ethane to methane ratios; propane to methane

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ratios) in the separator overhead for the relevant well site.

If a site

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Equipment Leaks The emission source category of equipment leaks includes any leaks from

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wellhead equipment, piping, flanges, fittings, valves, separators, and dehydrators. Allen et al.19

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reported a total of 278 equipment leaks, detected using an infrared camera, at 150 natural gas

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producing sites (total number of wells = 478). The average count of leaks per site was 1.85

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(range 0-12) and the average emission factor was 0.12 kg CH4/h per leak (range: 0.0-5.6 kg

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CH4/h).

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measurements reported by Allen, et al.19 were grouped into cohorts based on number of wells at

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each site.12 Each of the 17,400 production sites in the Barnett Shale were assigned a number of

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leaks sampled from appropriate well per site cohorts, and then emissions for the leak were

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sampled from the measured emission factors. Emissions were assumed to be continuous. The

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assumed composition of the emissions was randomly selected, with equal probabilities, from

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separator overhead, separator feed, condensate tank flash and produced water tank flash emission

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compositions, if both condensate and water are produced at the site. If any of the liquids were not

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produced at a site, the composition was randomly selected from one of the emissions categories

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that did occur at the site.

In order to account for lower number of wells per site in Barnett Shale region,

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Compression Systems The frequency of compression systems being present at production sites in

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the region was estimated to be 0.14 per well in the Barnett Shale region, based on data from the

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GHGRP.3 The average number of compressors per production site that had compressors was

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modeled as 1.2 with a range of 1 to 5 based on data from the Texas Commission on

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Environmental Quality’s 2009 Barnett Shale Special Emissions Inventory (BSEI).6 Sites for

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compressors were randomly selected. A site was assigned a number of compressors based on the

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distribution of compressor counts per site in the Barnett Shale Special Emissions Inventory until

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the total count of compressors for the region matched the estimated region total. Fugitive

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emissions, engine exhaust, compressor start-ups and compressor blowdowns were estimated for

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each compressor. Fugitive emissions from compressors were assumed to be continuous and were

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estimated to have a mean value of 0.24 kg CH4/hr with a 95% confidence interval of 0.16-0.32

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kg CH4/hr for each compressor.20 Emissions for each compressor were selected randomly from

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the distribution, assuming that the emissions per compressor were normally distributed. The

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separator overhead composition was assumed for compressor fugitive emissions. Compressor

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exhaust emissions were assumed to be continuous and were estimated based on engine

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horsepower data (N = 1,100; mean = 156 HP; range of 10-1,340 HP) (TCEQ BSEI). This

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distribution was sampled with replacement to assign a specific horsepower to each compressor.

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An emission rate for CH4 in engine exhaust was based on manufacturer specifications for the

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Caterpillar 3306, which represents about 75% of reported engines in the Barnett Shale Special

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Emissions Inventory, augmented by 25% to reflect real-world performance. A distribution of ±

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50% around this central value was used to account for potential variability in operation and

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maintenance. This resulted in a central emission factor of 0.0013 kg CH4/h per HP-hr (95%

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confidence interval: 0.0005-0.002 kg CH4/h per HP-hr). Emissions for each compressor were

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sampled from a normal distribution with those characteristics. The composition of separator

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overhead was assumed for the compressor exhaust, reflecting an assumption that hydrocarbon

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emissions would have a composition similar to unburned fuel.

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blowdowns were modeled as intermittent emissions. Zavala-Araiza et al.12 modeled occurrence

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of compressor startup events assuming that start up can occur during any working hour of the

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year and startup events last one hour. For 3,600 compressors in the region, the expected number

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of compressors under startup at any given time was 10 with 95% CI of 4-17. The number of

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compressor startups in the region in any single hour was sampled from this normal distribution. 8


Compressor start-ups and

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For each compressor start-up an emission rate was sampled from a normal distribution of mean

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12 kg CH4 per start-up (95% CI 6.1-18 kg CH4).21

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composition. The same approach was used to model compressor blowdowns. The regional

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count of blowdown events at any given hour was 9.0 with 95% CI of 4 to 15, and emissions

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averaged 6.5 kg CH4 per blowdown (95% CI 3.3-9.8 kg CH4).21 Compressor blow down event

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duration was assumed to be one hour. Separator overhead was assumed for the composition.

Separator overhead was assumed for the

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Oil/Condensate Flashing Zavala-Araiza et al.12 developed a time resolved model to estimate

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instantaneous emission rates from condensate flashing; these methods were adapted in this work

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to generate hourly emission rates. An intermittent discharge of 2-4 gallons of condensate from a

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separator to a tank (a separator dump) estimated by Zavala-Araiza, et al.12 was used here. This

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condensate flashes quickly, releasing methane and other light alkanes. If condensate production

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rate at a site was greater than 2-4 gallons every hour (exact values were selected randomly for

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each site assuming a normal distribution with a standard deviation of 0.5 gallon), the site was

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considered to have continuous flashing when emissions are reported on an hourly time scale.

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Otherwise the site was assumed to have intermittent flashing with the frequency of flashing

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determined by condensate production rate divided by dump volume. This threshold rate for

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continuous emissions was approximately 1.7 barrels per day (bbl/day) (95% CI: 1.2–2.3

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bbl/day). From a total of 4,829 sites with condensate production (range of 0.1-967 bbl/day;

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median of 1.1 bbl/day; and average of 6.9 bbl/day), continuous flashing was predicted to occur at

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1,963 sites (95% CI: 1,745-2,413 sites) based on the condensate production threshold.

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Emissions were calculated based on the amount of condensate being flashed, and the

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composition of the condensate phase exiting the separator. All of the methane, ethane, propane

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and butanes in the condensate tank were assumed to flash within an hour of being sent to the

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tank. Zavala-Araiza et al.12 estimated that 60% of condensate production in Barnett Shale was

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controlled by devices such as flares, combustors and vapor recovery units. Sites were sorted in

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descending order of condensate production and the top 60% of production was assigned control

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devices with a 98% control efficiency, which is typical of efficiencies assumed for flares. This

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corresponds to 254 sites (5% of condensate production sites) with a threshold production rate of

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27 bbl/day. Zavala-Araiza, et al.12 increased these emissions by a factor of 1.4 (mean 1.4, 95%

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CI: 1-3) to account for control technology effectiveness less than 98%; that enhancement factor

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is also included in this work, but as shown in the results section, does not significantly impact

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total emissions. Condensate tank flash composition was assumed. Since a vast majority of

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emissions from well sites observable with a helicopter mounted infrared camera come from

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tanks,9 a sensitivity analysis without controls on tanks was performed and is reported in

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Supporting Information.

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Water Flashing The assignment of water tank flashing to sites was modeled in manner similar to

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condensate tank flashing. Amounts of methane, ethane, propane and butanes dissolved in the

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water sent to the water tank were calculated using Henry’s Law coefficients (see Supporting

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Information). Flashing with continuous and intermittent temporal profiles leads to estimates of

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11,783 sites having emissions at any given hour (95% CI: 11,480-12,338 sites), with 8,858 sites

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(95% CI: 8,293-9,552) venting continuously and 2925 sites (95% CI: 1,928-4,045) venting

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intermittently. For the composition, water tank flash composition was applied.

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Liquid Unloading Zavala-Araiza et al.12 analyzed regional data for liquid unloadings from the

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GHGRP3 and estimated the average frequency of liquid unloading events during any given

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working hour of the year. An average of 2.4 unloading events occur per year at each of the 15%

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of wells that report unloading emissions. If an average duration of the events is an hour or less,

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this results in an estimated 2.1 wells unloading at any working hour (half of the hours in the day)

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with 95% CI of 0-5. To calculate emissions, the number of unloading events for the region was

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first determined from a normal distribution with mean 2.1. Emission rates were estimated based

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on the work of Allen, et al.22 which reports emissions for wells with and without plunger lifts.

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For the Barnett Shale, 60% of sites with unloading emissions are assumed to have no plunger.

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The average emission of wells without plunger-lift was 486 kg CH4 per event (range: 10.7–2,600

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kg CH4 per event); for the wells with plunger-lifts average emissions were 132 kg CH4 per event

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(range: 1.1–960 kg CH4 per event). Events were assumed to last for one hour or less for both

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manual and automated unloadings. For the composition, separator feed was assumed.

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Dehydrators Zavala-Araiza et al12 estimated 0.002 dehydrators per well for production sites in

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the region, based on dehydrators on production facilities in the 2014 GHGRP. This leads to an

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estimated 51 sites with dehydrators; sites were randomly selected from production site locations.

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Dehydrators can also be located at gathering sites, but the assumption of exactly where the

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dehydrators will be located will have minimal impact of the results for the Barnett Shale region.

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Emissions were sampled from 29 reported emission factors for the Barnett Shale (GHGRP),

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which had a mean of 0.083 kg CH4/hr, and a range of 0.0053 to 0.50 kg CH4/hr.12 For the

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composition, separator overhead was assumed.

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Pneumatic Controllers Allen et al.23 reported 377 measurements of emissions from pneumatic

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controllers. Emission rates varied depending on the type of device being controlled (e.g., level

308

control on a separator or temperature control on a process heater). Emissions were modeled for

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six applications; separator, process heater, compressor, dehydrator, wellhead, and plunger lift. In

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this work, emissions were sampled from subsets of measurements for each particular application.

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Counts of pneumatic controller per device (e.g. controllers per compressor) were estimated based

312

on data reported by Allen et al.23. Counts of devices per well are estimated with data in the

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GHGRP.

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Supporting Information. The composition of separator overhead was used for all pneumatic

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controllers, assuming that pressurized produced gas is used to drive the devices.

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Mid-Stream Sites Data for mid-stream sites (276 compression stations and 38 processing plants)

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were compiled by Lyon et al.13. Zavala-Araiza et al.10 constructed a log-normally distributed

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emission rate for compression stations and processing plants in the region. Emissions rates are

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generated for mid-stream stations and gas processing plants with random selection from the log-

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normal model. It has been assumed that the top 1% of emissions from the distribution are

321

intermittent, and the rest are treated as continuous. Pipeline gas composition, typical of emissions

322

after gas processing, is assumed for the emissions.

323

Information.

324

Pre-production emissions: Pre-production emissions of light alkanes were assumed to be due to

325

completion flowbacks. Emission locations were randomly selected from compilations of wells

326

completed in 2013.14,18 If the 563 wells completed in 2013 each had completion emissions for an

327

average of one week (168 hours), then the average number of completions active in any given

328

hour was 11. Emissions per completion event were selected based on emission factors used in

329

the US EPA’s Greenhouse Gas Inventory.24 The EPA identifies separate emission factors for

330

wells that flare and wells that recover their completion emissions. Wells were randomly selected

331

to either flare or recover their emissions; emission factors for individual wells were selected from

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normal distributions with means of 5,900 kg methane per event for wells that flare (35.1 kg

333

CH4/hr; 95% CI: 17.9–52.3 kg CH4/hr) and 3,200 kg CH4 per event for wells that recover gas

334

(19.0 kg CH4 per hour; 95% CI: 9.7–28.4 kg CH4/hr).

Details of the pneumatic controller counts and emission factors are provided in

Details are provided in Supporting

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High-emitting sources Zavala-Araiza et al.12 concluded that a group of “super-emitters”, which

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are not accounted for using equipment counts and emission factor distributions, emit an average

338

of approximately 11,000 kg/hr of methane. The range of magnitudes and durations of these

339

emission sources, along with their underlying causes, remain uncertain and the source category

340

of high-emitting source or super-emitter has no universally accepted definition. In this work, the

341

definition of high-emitters is taken from Zavala-Araiza, et al.12 For the sake of transparency in

342

this work, emission scenarios will be run both with and without these emissions. When these

343

emissions are included, it will be assumed that the region-wide total for these emissions is

344

precisely 11,000 kg/hr. It will be assumed that in any hour, a total of 100 sites will have these

345

emissions, each site accounting for 110 kg/hr, and the location of these sites will be randomly

346

selected; these sources are assumed to be intermittent and will change location on each hourly

347

instance of emissions. The number of sites is approximately equivalent to the number of high

348

emitting production sites estimated by Zavala-Araiza, et al.10 The composition of the emissions

349

will be assumed to be equivalent to separator feed and separator overhead (assumed equivalent in

350

this work; see Supporting Information).

351 352

13



Page 14 of 29

353

Results and Discussion

354

Emissions of methane, from oil and gas sources, for the entire Barnett Shale domain, by source

355

category, are reported in Table 1. Similar tabulations of emissions for ethane, propane and

356

butanes are provided in Supporting Information, as well as comparison to other emission

357

inventories of methane emissions. Continuous and intermittent emissions are reported separately

358

in Table 1 for each source. Some source categories have emissions that are modeled either as

359

entirely intermittent (e.g., liquid unloadings) or entirely continuous (e.g., chemical injection

360

pumps). Other source categories, such as compressors, have some emissions that are continuous

361

(e.g., combustion emissions) and other components that are intermittent (e.g., blowdowns and

362

start-ups). Overall, emissions that are represented as intermittent emissions represent 13-30% of

363

the methane emissions in the inventory, depending on whether the high emitting source category

364

is included in the inventory.

365

based on distributions of emissions by source type, both continuous and intermittent sources

366

exhibit variability in estimated emissions from hour to hour. Means, confidence intervals (95%),

367

maximum and minimum values of emission rates are estimated for each set of 1,000 instances of

368

the Monte Carlo simulations, and averages of these statistics from the ten sets of 1,000 hourly

369

instances are reported in Table 1 for an inventory of working hours. Inventory data for non-

370

working hours are reported in Supporting Information. The mean and confidence intervals were

371

highly consistent across the ten sets of 1,000 hourly instances of the inventory, but the maximum

372

and minimum values across the ten sets of simulations have higher variability, so the maximum

373

and minimum of 10,000 hourly instances is also reported. The average spatial distributions of

374

emissions for methane and ethane are shown in Figure 2. The results reported in Figure 2

375

include high-emitting sources.

376

The largest sources of methane emissions are mid-stream sites (compressor stations and gas

377

processing plants), and pneumatic devices (pumps and controllers). Collectively, these sources

378

account for 83% of mean emissions when high-emitters are not included in the total. Estimated

379

variability is dominated by the relatively small number of mid-stream sites. Variability due to

380

mid-stream sites is within 10% of total variability, when high-emitters are not included in the

381

total. This is because the summed, hourly average emissions of a large number of pneumatic

382

controllers have a much lower overall variability than the variability in emissions of any single

Because emission factors were not fixed values, but rather were

14


Page 15 of 29


383

controller. In contrast, the emissions of the much smaller number of mid-stream sites will be

384

much less moderated by summing the emissions over all mid-stream sources.

385

15



Page 16 of 29

386

An important use for hourly emission inventories is in reconciling aircraft measurements with

387

emission inventories. Multiple aircraft measurements have been performed in the Barnett Shale

388

oil and gas production region, including measurements of both methane and ethane.25,26 Figure 3

389

shows locations of emission sources and flight tracks for measurements of methane and ethane in

390

the Barnett Shale region. Figure 3 also shows, conceptually, the method that was used in

391

comparing emissions to flight data. Back trajectories from the flight track, parallel to the wind

392

direction, are shown as parallel lines in Figure 3b. Emission sources located on the back

393

trajectory lines were summed to predict methane and ethane emissions for each flight. A

394

distribution of the total emissions expected to contribute to observations along each flight track is

395

created from the ten Monte Carlo simulations, by averaging the statistics of each simulation. This

396

distribution is reported in Figure 4 along with measurements from individual flights. Predictions

397

of the emission inventory are not affected by mixing height, as emission rates are being summed,

398

but this variable is embedded in the results of the flights since measured concentrations are

399

transformed to emission rates (fluxes).26 In Figure 4, emission inventories associated with the

400

flights on 3/27, 3/30, and 10/25 are higher than for the flights on 10/16, 10/19, 10/20 and 10/28

401

because a larger fraction of the sources are upwind of the flight tracks on those days. All of the

402

inventories associated with the flight tracks have relatively wide 95% confidence intervals for

403

methane, averaging 15,800 kg/h for methane (28% of the mean value of the inventory). The

404

difference between maximum and minimum values (average of the ten Monte Carlo simulations

405

with 1,000 instances of hourly emissions each) was two and a half times the 95% confidence

406

range. This broad range of emissions, expected based on the intermittency of some sources and

407

the distributions expected in emission factors, is important to consider in reconciling emission

408

inventories with observations. Two of the aircraft observations have lower methane emission

409

rates than those predicted by the emission inventory, while four of the aircraft observations have

410

emissions higher than the 97.5th percentile predicted by the emission inventory, however, all of

411

the mean methane emission estimates based on aircraft observations are within the extreme

412

values predicted for the inventory.

413 414

For ethane, the ninety-five percent confidence interval is narrower (900 kg/hr, 15% of the mean),

415

because midstream sources, a major contributor to mean methane emissions and variability in

416

methane emissions, has relatively low ethane content. Also shown in Figure 4 is the variability 16


Page 17 of 29


417

in the ethane to methane ratio, a quantity frequently used in allocating emissions to oil and gas

418

sources.27 This ratio has 95% confidence intervals that average 20% of the mean, and mean

419

values that vary from day to day, based on the location of the flight track.

420 421

The ethane emissions predicted by the inventories can be directly compared to the emissions

422

predicted based on the aircraft data. Smith, et al.25 report ethane emissions that averaged 6,600 ±

423

200 kg/hr (standard deviation) for six of the flights; the prediction of the inventory for the same

424

flights was 5,050 kg/hr, using the average of the means of the ten Monte Carlo simulations in

425

each grid cell; by comparison, the emission inventory predicts that the whole basin emits an

426

average of 6,200 kg/hr of ethane (95% CI: 5,800-6,700). The emission inventory emission rates

427

are lower than the measured values, even if the emissions for the whole basin are compared to

428

the measurements. Comparison of the individual flights indicate that there is some systematic

429

bias in the results, as all of the emission inventory predictions are lower than the measurements.

430

The flight of 10/25 has a higher measured rate of ethane than the maximum value of the 10,000

431

hourly instances of the emission inventory. This flight also had high emissions of methane

432

estimated based on aircraft observations, suggestive of a large emission event. Comparisons of

433

inventory predictions of methane emissions to methane emissions based on aircraft observations

434

are more difficult than for ethane, because landfills, animal husbandry and other sources

435

contribute to the methane observations. One notable feature of the aircraft data, however, is the

436

greater flight to flight variability in the methane emission estimates, when compared to the

437

ethane emission estimates (CIs in the aircraft data are 35% of mean values for methane,

438

compared to 6% of mean values for ethane). The inventories also predict this difference in

439

variability.

440

suggests that the variability is due to sources with low ethane to methane ratios. For oil and gas

441

sources, midstream sources have this feature, and the inventories developed in this work predict

442

that these mid-stream sources will dominate variability in location and time specific inventories

443

in the Barnett Shale.

444

particularly important in the Barnett Shale region, may not be as important for regions where

445

large, intermittent sources of emissions at well sites (e.g., liquid unloadings) are more common

446

than in the Barnett Shale. Nevertheless, the type of emission estimation approach described in

This consistent difference in emission inventory and flight to flight variability

Mid-stream emission variability, which this analysis suggests is

17



447

this work for the Barnett Shale, when applied to other regions, could be used to characterize

448

emission variability.

Page 18 of 29

449 450

The emission inventory modeling approach described in this work allows for multiple types of

451

sensitivity analyses to be performed. For example, if times and locations of intermittent sources

452

are known (e.g., locations and times of compressor blowdowns, liquid unloadings and high-

453

emitters), they can be explicitly modeled; the sensitivity of emissions to alternative emission

454

factor distributions or chemical compositions of emitted streams can also be assessed, and

455

inherent day to day variability in emissions can be accounted for; the effectiveness of emission

456

controls (e.g., condensate flashing controls for tanks) can be modified based on the actual

457

controls if data are available. Examples of these sensitivity studies are reported in Supporting

458

Information. Overall, this approach to emission inventory development offers a novel approach

459

to reconciling inventory information with short duration ambient observations. As with other

460

emission inventories, inherent uncertainty remains, for example in the activity factors retrieved

461

from GHGRP, which other studies have found could be a limitation in accurately estimating

462

emissions,28 however, this emission inventory uses the best available data to generate estimates

463

of spatial and temporal variability in emissions, which is important to take into account in source

464

attribution and photochemical modeling.

465 466 467 468 469

Supporting Information

470

This paper has Supporting Information describing (i) Molecular Composition of Oil and Gas

471

Emission Sources; (ii) Pneumatics and Mid-Stream Emission Estimates; (iii) Ethane, Propane

472

and Butane Emission Estimates; (iv) Sensitivity Analyses; (v) Non-Working Hours Emission

473

Inventory; (vi) Emissions of Higher Molecular Weight Hydrocarbons and Air Toxics; (vii)

474

Methane Emission Comparison to Other Studies.

475

animations of multiple instances of the hourly emission inventory. This information is available

476

free of charge via the Internet at http://pubs.acs.org.

Supporting Information also includes

477

18


Page 19 of 29


478

Acknowledgements

479

Funding to perform the analyses reported here was provided by ExxonMobil Upstream Research

480

Company. The authors also thank Dr. Daniel Zavala-Araiza of Environmental Defense Fund for

481

many helpful discussions.

482 483

Disclosures

484

The authors declare the following competing financial interest(s): Funding to perform the

485

analyses reported here was provided by ExxonMobil Upstream Research Company. In addition,

486

the lead author (DTA) has served as chair of the Environmental Protection Agency’s Science

487

Advisory Board (2012−2015; in this role, he was a paid Special Governmental Employee); he

488

has done work as a consultant for multiple companies, including Eastern Research Group,

489

ExxonMobil, and Research Triangle Institute. Over the past five years, he has also worked on

490

methane emission measurement projects that have been supported by multiple natural gas

491

producers and Environmental Defense Fund.

19



492 493 494 495

References 1. US Environmental Protection Agency. National Emission Inventory, 2014. http://www.epa.gov/air-emissions-inventories/national-emissions-inventory (accessed February, 2017).

496 497 498

2. U.S. Environmental Protection Agency. Draft Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015, EPA 430-P-17-001. https://www.epa.gov/ghgemissions/inventory-usgreenhouse-gas-emissions-and-sinks (accessed February, 2017).

499 500

3. U.S. Environmental Protection Agency. Greenhouse Gas Reporting Program. https://www.epa.gov/ghgreporting (accessed February, 2017).

501 502 503 504

4. Pennsylvania Department of Environmental Protection (PA DEP, 2016). Draft 2016 Pennsylvania Greenhouse Gas Emission Inventory. http://files.dep.state.pa.us/Air/AirQuality/AQPortalFiles/Advisory%20Committees/CCAC/2016/ 7-12-16/Emission_Inventory_for_7-12_CCAC_meeting_R1.pdf (accessed February, 2017).

505 506

5. California Air Resources Board (CARB) 2016 California Greenhouse Gas Emission Inventory. https://www.arb.ca.gov/cc/inventory/data/data.htm (accessed February, 2017).

507 508 509 510

6. Texas Commission on Environmental Quality. Barnett Shale Special Inventory, Phase Two Workbook (Excel). http://www.tceq.texas. gov/assets/public/implementation/air/ie/pseiforms/bshaleworkbook.xls (accessed February, 2017).

511 512 513 514

7. Maasakkers, J.D.; Jacob, D.J.; Sulprizio, M.P., Turner, A.J.; Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A.A., Bowman, K.W., Jeong, S., Fischer, M.L. Gridded National Inventory of Methane Emissions. Environ. Sci. Technol. 2016, 50, 13123–13133; DOI 10.1021/acs.est.6b02878.

515 516

8. U.S. Environmental Protection Agency. SPECIATE Version 4.4. https://www.epa.gov/airemissions-modeling/speciate-version-45-through-32 (accessed February, 2017).

517 518 519

9. Lyon, D.R.; Alvarez, R.A.; Zavala-Araiza, D.; Brandt, A.R.; Jackson, R.B.; Hamburg, S.P. Aerial Surveys of Elevated Hydrocarbon Emissions from Oil and Gas Production Sites, Environ. Sci. Technol. 2016, 50, 4877−4886; DOI 10.1021/acs.est.6b00705.

520 521 522 523

10. Zavala-Araiza, D.; Lyon, D. R.; Alvarez, R. A.; Davis, K. J.; Harriss, R.; Herndon, S. C.; Karion, A.; Kort, E. A.; Lamb, B. K.; Lan, X.; et al. Reconciling divergent estimates of oil and gas methane emissions. Proc. Natl. Acad. Sci. 2015, 112 (51), 15597–15602 DOI: 10.1073/pnas.1522126112.

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11. Littlefield, J.A; Marriott, J., Schivley, G.A,; Skone, T.J. Synthesis of Recent Ground-Level Methane Emissions Measurements from the U.S. Natural Gas Supply Chain. Journal of Cleaner Production. 2017, 148, 118-126 DOI: 10.1016/j.jclepro.2017.01.101

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12 Zavala-Araiza, D.; Alvarez, R.A.; Lyon, D.R.; Allen, D.T.; Marchese, A.J.; Zimmerle, D.J.; Hamburg, S.P. Super-emitters in natural gas infrastructure are caused by abnormal process conditions. Nat. Commun. 2017, 8, 14012.

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13. Lyon, D. R.; Zavala-Araiza, D.; Alvarez, R. A.; Harriss, R.; Palacios, V.; Lan, X.; Talbot, R.; Lavoie, T.; Shepson, P.; Yacovitch, T. I.; et al. Constructing a Spatially Resolved Methane

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Emission Inventory for the Barnett Shale Region. Environ. Sci. Technol. 2015, 49 (13), 8147– 8157 DOI: 10.1021/es506359c.

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14. Texas Railroad Commission, Production Data Query System. January, 2017. http://www.rrc.state.tx.us/media/22204/barnettshale_totalnaturalgas_day.pdf (accessed February 2017).

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15. Steward, M.; Arnold, K. Gas-Liquid and Liquid-Liquid Separators. Gulf Professional Publishing: Oxford, 2009.

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16. Abdel-Aal, H. K.; Aggour, M.; Fahim, M. A. Petroleum and gas field processing. CRC Press: Florida, 2016.

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17. Eastern Research Group, Inc. Condensate tank oil and gas activities: Attachment D. Final Report to the Texas Commission on Environmental Quality Air Quality Division. Available at: https://www.tceq.texas.gov/airquality/airmod/project/pj_report_ei.html (accessed November, 2016).

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18. Drillinginfo. DI Desktop; Austin, TX, 2015. http://www.didesktop.com/ (accessed March 1, 2015).

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19. Allen, D.T.; Torres, V.M.; Thomas, J.; Sullivan, D.; Harrison, M.; Hendler, A.; Herndon, S.C.; Kolb, C.E.; Fraser, M.; Hill, A.D.; Lamb, B.K.; Miskimins, J.; Sawyer, R.F.; Seinfeld, J.H. Measurements of Methane Emissions at Natural Gas Production Sites in the United States, Proc. Natl. Acad. Sci. 2013, 110, 17768-17773 DOI: 10.1073/pnas.1304880110.

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20. US Environmental Protection Agency. U.S. Greenhouse Gas Inventory Report 1990-2013. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-19902013 . (Accessed February, 2017).

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21. Harrison, M.; Shires, T. EPA GRI. Methane Emissions from the Natural Gas Industry Volume 7: Blow and Purge Activities. Technical report for Gas Research Institute and US Environmental Protection Agency: Austin, TX, 1996. https://www.epa.gov/sites/production/files/2016-08/documents/7_blowandpurge.pdf (Accessed February, 2017).

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22. Allen, D.T.; Sullivan, D.; Zavala-Araiza, D.; Pacsi, A.; Harrison, M.; Keen, K.; Fraser, M.; Hill, A.D.; Lamb, B.K.; Sawyer, R.F.; Seinfeld, J.H. Methane Emissions from Process Equipment at Natural Gas Production Sites in the United States: Liquid Unloadings, Environ. Sci. Technol. 2015, 49 (1), 641–648 DOI: 10.1021/es504016r.

563 564 565 566

23. Allen, D.T.; Pacsi, A.; Sullivan, D.; Zavala-Araiza, D.; Harrison, M.; Keen, K.; Fraser, M.; Hill, A.D.; Sawyer, R.F.; Seinfeld, J.H. Methane Emissions from Process Equipment at Natural Gas Production Sites in the United States: Pneumatic Controllers, Environ. Sci. Technol. 2015, 49 (1), 633–640 DOI:10.1021/es5040156.

567 568 569 570

24. US Environmental Protection Agency. Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Revision to Hydraulically Fractured Gas Well Completions and Workovers Estimate. https://www3.epa.gov/climatechange/pdfs/HF-Gas-well-completion-workover-updatememo-4-10-2015.pdf (accessed February, 2017).

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571 572 573

25. Smith, M. L.; Kort, E. A.; Karion, A.; Sweeney, C.; Herndon, S. C.; Yacovitch, T. I. Airborne Ethane Observations in the Barnett Shale: Quantification of Ethane Flux and Attribution of Methane Emissions. Environ. Sci. Technol. 2015, 49, 8158−8166.

574 575 576 577 578

26. Karion, A.; Sweeney, C.; Kort, E. A.; Shepson, P. B.; Brewer, A. Cambaliza, M.; Conley, S. A.; Davis, K.; Deng, A.; Hardesty, M.; Herndon, S. C.; Lauvaux, T.; Lavoie, T.; Lyon, D.; Newberger, T.; Petron, G.; Rella, C.; Smith, M.; Wolter, S.; Yacovitch, T. I.; Tans, P. Aircraftbased Estimate of Total Methane Emissions from the Barnett Shale Region. Environ. Sci. Technol. 2015, 49, 8124−8131.

579 580

27. Allen, D.T. Attributing Atmospheric Methane to Anthropogenic Emission Sources, Acc. Chem. Res. 2016, 49, 1344-1350 DOI: 10.1021/acs.accounts.6b00081.

581 582 583 584

28. Zimmerle, D.J.; Williams, L.L.; Vaughn, T.L.; Quinn, C.; Subramanian, R.; Duggan, G.P.; Willson, B.; Opsomer, J.D.; Marchese, A.J.; Martinez, D.M.; Robinson, A.L. Methane Emissions from the Natural Gas Transmission and Storage System in the United States. Environ. Sci. Technol. 2015, 49, 9374-9383. DOI: 10.1021/acs.est.5b01669

585

22


Page 22 of 29

Page 23 of 29

586 587


Figure 1. Flows at a representative oil and gas production site

588 589

23



590 591 592

Table 1. Methane emissions for entire Barnett Shale domain, reported by source category, based on 10,000 instances of an hourly emission estimate Source

Chemical Inj. Pumps Equipment Leaks Compressio n Systems Condensate Flashing Water Flashing Liquid Unloading Dehydrators Pneumatic Controllers Mid-Stream Sites Preproduction emissions Total w/o highemitters Highemitters Total w/ highemitters 593 594 595 596 597 598

Page 24 of 29

Mean of Continu ous Emissio nsa

Mean of Intermit tent Emissio nsa

Mean of Total Emissionsa

95% CI (Total Emissio ns)a

Min (Total Emissions )a

Max (Total Emissions)a

(methane emissions in kg/hr)c 3,0002,900 3,200 3,200

Min (Total Emissions) from 10,000 instancesb

Max (Total Emissions) from 10,000 instancesb

2,900

3,300

3,100

0

3,100

2,600

0

2,600

2,4002,700

2,400

2,800

2,300

2,800

1,600

190

1,800

1,7001,800

1,600

1,900

1,600

1,900

670

90

760

740-770

730

790

720

790

1,200

4

1,200

1,200

1,200

1,200

1,200

1,200

0

740

740

0-2,900

0

5,400

0

6,500

6

0

6

4-7

4

8

3

9

9,200

0

9,200

9,1009,400

9,000

9,500

8,900

9,600

20,000

4,700

25,000

19,00034,000

16,000

56,000

15,000

97,000

0

300

300

230-370

200

420

190

440

39,000

6,000

45,000

38,00054,000

35,000

76,000

34,000

120,000

0

11,000

11,000

11,00011,000

11,000

11,000

11,000

11,000

39,000

17,000

56,000

49,00065,000

46,000

87,000

45,000

130,000

a

Mean, confidence interval, maximum and minimum values are averages from ten different Monte Carlo simulations of 1,000 hourly instances of the inventory. Variability of the mean and confidence intervals across the ten Monte Carlo simulations is within 5% with respect to the mean values of these statistics, for methane emissions. bMin and max values of the 10,000 hourly instances. cSurface area of the non-zero grid cells (see Figure 2) is ~ 28,000 km2.

24


Page 25 of 29

599 600


Figure 2. Spatial distribution of methane (a) and ethane (b) emissions, on 4 x 4 km grid cells, based on an average of 1,000 instances of an hourly emission estimate a)

601

b)

602 603

25



604 605 606 607

Page 26 of 29

Figure 3. (a) Locations of flight tracks in the Barnett Shale with emission source locations (b) Back trajectories, parallel to the wind direction (shown with the red arrow), were used to establish which sources contributed to individual flight observations; the October 19th, 2013 flight is shown as an example. a)

608

b)

609 610

26


Page 27 of 29

611 612 613 614 615 616 617 618 619 620 621 622 623


Figure 4. (a) Methane emissions for seven flights in the Barnett Shale; (b) Ethane emissions for the seven flights; (c) Ethane to methane ratios for the flights, based on the emission inventory. Quantile statistics are presented for the average of ten Monte Carlo simulations, with 1,000 instances each. Median values, 25th and 75th percentiles (edges of boxes), 2.5th and 97.5th percentiles (edges of whiskers) and minimum and maximum values (points) for the average of the ten Monte Carlo simulations. Minimum and maximum values of the 10,000 hourly instances of the inventory, across all ten Monte Carlo simulations of 1,000 instances each, are denoted by triangles. In (a) and (b), for each flight day, the emission inventory distribution is plotted on the left and the aircraft measurement estimate is shown in the right with its standard deviation as reported by Smith et al.25. Methane emissions from all sources reported by Smith, et al.25 are multiplied by 0.78, which is the midpoint of the contribution to methane emissions from oil and gas sources in Barnett Shale attributed by Smith (range of 0.71 to 0.85). (a)

(b)

624 625

(c)

27



626 627

28


Page 28 of 29

Page 29 of 29

628 629 630


Table of Contents Art

631 632

29


Variability in Spatially and Temporally Resolved Emissions and

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