June 2017
Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016:
Updates Under Consideration for Natural Gas and Petroleum Systems Uncertainty
Estimates
The most recent uncertainty analysis for the natural gas and petroleum systems emissions estimates in
the Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) was conducted for the 1990-2009
GHGI that was released in 2011. The analysis was based on a detailed assessment of the activity data
and emission factor data available at that time. Since the analysis was last conducted, several of the
methods and data sources used in the GHGI have changed, and industry practices and equipment have
evolved. In addition, new studies and other data sources such as the EPA Greenhouse Gas Reporting
Program (GHGRP) have provided more information on emissions and the underlying conditions that lead
to emissions.
For the 1990-2016 GHGI (to be finalized in 2018), EPA is considering updates for the natural gas and
petroleum systems uncertainty analysis to reflect new information and revised Inventory
methodologies, and is seeking feedback on the updates. This memorandum provides general
background on uncertainty in the GHGI, documents the previous approach to calculating uncertainty
parameters, discusses a proposed updated approach for conducting the updated uncertainty analysis,
and requests stakeholder feedback on the updated approach. Note that the analyses presented in this
memorandum reflect estimates and methodologies used in the 2017 GHGI; therefore, resulting
estimates are subject to change for the final 2018 GHGI.
Overview of Uncertainty Analysis in the GHGI
In conformance with the United Nations Framework Convention on Climate Change (UNFCCC) reporting
requirements, EPA follows the Intergovernmental Panel on Climate Change (IPCC) Guidelines for
National Greenhouse Gas Inventories (IPCC Guidelines, IPCC (2006)) to develop uncertainty estimates for
sources included in the national GHGI. The IPCC Guidelines note the essential role of uncertainty
estimates for guiding improvements to national inventories: "An uncertainty analysis should be seen,
first and foremost, as a means to help prioritize national efforts to reduce the uncertainty of inventories
in the future, and guide decisions on methodological choice. For this reason, the methods used to
attribute uncertainty values must be practical, scientifically defensible, robust enough to be applicable
to a range of categories of emissions by source and removals by sinks, methods and national
circumstances, and presented in ways comprehensible to inventory users."
The uncertainty analysis is performed by developing confidence intervals, which give the range within
which the "true" value of an uncertain quantity is thought to lie for a specified level of confidence. The
IPCC Guidelines suggest the use of a 95% confidence interval, which is the interval that has a 95%
probability of containing the unknown "true" value.
To develop a 95% confidence interval for an emission estimate from a chosen source category (e.g.,
natural gas systems), it is necessary to characterize the probability density function (PDF) of the average
emission and activity factors for each emission source contributing to that source category emission
estimate. The PDF describes the range and relative likelihood of possible values for the average emission
and activity factors corresponding to that emission source (e.g., reciprocating compressors in the natural
gas transmission segment). Ideally, the PDF would be derived from source-specific information.
However, in the absence of such data, it is also possible to use information developed through
elicitation of expert judgment (IPCC 2006).
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June 2017
Once the applicable PDFs are characterized, a Monte Carlo analysis can be conducted to characterize the
composite uncertainty for each emission source (e.g., national reciprocating compressor emissions in
the natural gas transmission segment) as well as the overall source category (e.g., national natural gas
system emissions). Although default uncertainty values are provided by IPCC and propagation of error is
a valid approach, the Monte Carlo approach is more rigorous and recommended for sources that use
more sophisticated estimation methodologies, where PDFs may be non-normal, and if uncertainties are
large (IPCC 2006). As described in the IPCC guidelines, Monte Carlo analysis involves selecting random
values for emission factors and activity data from the respective PDFs and calculating the resulting
emission estimate. This procedure is repeated numerous times and the results of each simulation are
used to characterize the PDF for the overall emission estimate for the source category (IPCC 2006).
Figure 1 depicts the steps involved in conducting a Monte Carlo analysis. From the figure, only Steps 1
and 2 require user input (e.g., specification of PDFs for emission and activity factors); Steps 3 through 5
are conducted through use of a software package such as @RISK.
To develop uncertainty bounds around total estimated emissions at the national level, the source-
specific emissions that correspond to the lower and upper confidence bounds can be summed, as the
total national level estimate is the simple sum of source-specific emissions.
The approach described estimates the national emissions associated with a source category as the
product of the average emission factor and average activity factor for that source category. While recent
studies show that the emissions from certain source categories may be highly skewed with potentially
fat right-hand tails due to the existence of "super-emitters," per the Central Limit Theorem (CLT), the
impact of such is expected to be minimal on the average emission factors and average activity factors
used in calculating national estimated emissions (see section "Updated Uncertainty Analyses for Natural
Gas and Petroleum Systems in the 2018 GHGI: Approach" below).1
1 The Central Limit Theorem (CLT) states that the means of random samples drawn from a population with any
type of distribution will be normally or near-normally distributed, provided that the sample on which these factors
are based are unbiased (e.g., each population element, such as a facility or device, has an equal probability of
being sampled) and is of sufficient size (Mendenhall, Wackerly, & Scheaffer, 1990). The distribution of sample
means referred to in the CLT is different than a population distribution; the underlying population from which the
random samples are drawn may be non-normal, however the means of random samples from that distribution can
still be normally distributed as implied by the CLT.
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Figure 1: Illustration of Monte Carlo Method (Adapted from IPCC 2006)
This example assumes three emission sources each where the emission is calculated as Activity Data ¦ Emission Factor
Steps 1 and 2 - Specify uncertainties, width, and PDFs for all input data
Source A
Source B
Source C
EF Va ue
AD Va ue
EF Va ue
AD Va ue
EF Va ue
AD Va ue
Step 3 - Select values for variables from the probability distributions
JSelect random value|J
of Emission Factor
from distribution
Select random value
of Activity Data from
distribution
|Select random value|
of Emission Factor
from distribution
Select random value
of Activity Data from
distribution
|Select random value|
of Emission Factor
from distribution
Select random value I
of Activity Data from
distribution

SteD 4 - Calculate emissions
Estimate Emissions
by multiplying
numbers
Estimate Emissions
by multiplying
numbers
Estimate Emissions
by multiplying
numbers
Add emission sources to
give source category total

Step 5 - Iterate and monitor results
Store emissions
total in database i
of results
Calculate overall mean
and uncertainty from
database of results
Repeat Step 3
More iterations
Repeat until mean and
distribution do not change

Finished
Distribution of final results I
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June 2017
Background on Uncertainty for Natural Gas and Petroleum Systems
EPA conducted the last complete uncertainty analyses for natural gas and petroleum systems for the
1990-2009 GHGI that was released in 2011. For that analysis, EPA obtained many of the emission factors
and associated uncertainty parameters (e.g., PDF and standard deviation) from the 1996 EPA-Radian
study of the natural gas industry and the 1999 EPA-Radian study of the petroleum industry. EPA adopted
the same source category-level uncertainty intervals for natural gas and petroleum systems emission
estimates subsequent to the 1990-2009 GHGI.
Basis of the 2011 GHGI Natural Gas Systems Uncertainty Analysis
The 2011 GHGI uncertainty analysis for natural gas systems included a detailed analysis for the twelve
top-emitting sources in 2009 (ranked according to the 2011 GHGI estimates), in which all elements of
each emission source estimate were defined in the uncertainty analysis. For the remaining sources, EPA
employed a simpler methodology as described in further detail below. For natural gas systems,
calculations are commonly more complex than simply multiplying an emission factor by an activity
factor. For example, the activity data calculation for production site upset emissions from pressure relief
valves (PRVs) involves three distinct elements: count of PRVs associated with all gas wells as originally
estimated in the 1996 EPA-Radian study and updated by EPA in 2007; NEMS region-specific fraction of
all gas wells for a given year; and the ratio of total gas wells in a given year compared to that in year
1992.
Table 1 provides the twelve top-emitting natural gas sources along with their year 1992 emissions used
in the 2011 uncertainty analysis. As can be observed from the table, EPA examined individual emission
sources at the NEMS region level for the production segment (due to the calculation methodology
varying by region for many production sources), and at the national level for other segments.
Although the top twelve sources were identified based on the year 2009 emissions estimate, EPA
conducted the actual uncertainty analysis on estimates for the year 1992, because it was the base year
(i.e., year of key input data collection) of the emissions and activity data estimates for many emission
sources. To define the uncertainty model parameters (steps 1 and 2 in Figure 1) of every element of the
activity and emission factors for the top twelve sources, EPA combined judgments of an industry expert
and a statistical expert along with data published in the 1996 EPA-Radian study. For all top twelve
sources as well as the remaining sources (that were analyzed using a simplified methodology), EPA
assumed a lognormal PDF as default. Then using the Monte Carlo simulation method in @RISK (steps 3
through 5 in Figure 1), EPA calculated the upper and lower estimates representing the 95% confidence
interval for each of the top twelve sources listed in Table 1.
These top twelve sources contributed nearly 49% of the total 1992 methane emissions from natural gas
systems. For the hundreds of non-top-twelve sources collectively representing approximately half of
natural gas systems emissions, EPA evaluated uncertainty using a simplified method which involved
assigning uncertainty values to each source activity and emission factor but not to the activity drivers
associated with that source. This simplified method does not completely capture the uncertainty
associated with all the sources but does ensure that the uncertainty of the sources that are not among
the top twelve is represented; assuming activity drivers have associated uncertainty, this approach
would lead to underestimating overall uncertainty. Also, using the Monte Carlo simulation method in
@RISK, EPA calculated the upper and lower estimates representing the 95% confidence interval for the
non-top twelve sources collectively.
To develop the uncertainty bounds for 1992, EPA compiled the upper and lower modeled estimates for
the top twelve and non-top twelve sources and then translated these figures to +/- percentages of the
GHGI estimate. EPA calculated the 95% confidence interval for natural gas systems emissions for 1992 at
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June 2017
-19% and +30% of the GHGI-reported value. EPA then assumed that the 95% confidence interval for
each of the other years was equivalent to these +/- percentage values.
Table 1. Top 12 Emission Sources for Natural Gas Systems in Previous (2011) Uncertainty Analysis for
	GHG Inventory Published in 2011 (2011GHGI)	

2011 GHGI CH4

Emissions, year 1992
Source
(MMT CChe)
Liquids Unloading (production segment, North East region)
34.8
Reciprocating Compressor Fugitives (transmission segment)
18.6
Liquids Unloading (production segment, Gulf Coast region)
17.5
Reciprocating Compressor Fugitives (processing segment)
8.1
Liquids Unloading (production segment, Mid Central region)
7.9
Shallow Water Offshore Platforms (production segment)
7.4
Wet Seal Centrifugal Compressors (transmission segment)
6.2
Pneumatic Controllers (production segment, Mid Central region)
5.6
Liquids Unloading (production segment, Rocky Mountain region)
3.4
Pneumatic Controllers (production segment, Rocky Mountain region)
2.1
Unconventional Gas Well Workovers (production segment, Rocky Mountain region)
0.0
Unconventional Gas Well Workovers (production segment, South West region)
0.0
Other Emission Sources
116.8
Total Potential Emissions from Natural Gas Systems (before Gas STAR reductions)
228.4
Basis of the 2011 GHGI Petroleum Systems Uncertainty Analysis
The 2011 GHGI uncertainty analysis for petroleum systems included a detailed analysis for the seven
top-emitting sources in 2009 (ranked according to the 2011 GHGI estimates), in which all elements of
each emission source estimate were defined in the uncertainty analysis. As with natural gas systems,
calculations of emission estimates for petroleum systems sources are more complex than simply
multiplying an emission factor by an activity factor. They usually involve additional data elements for
which PDFs need to be estimated for uncertainty analysis purposes.
Table 2 provides the seven top-emitting petroleum sources along with their year 1995 emissions used in
the uncertainty analysis.
Although the top seven sources were identified based on the year 2009 emissions estimate, EPA
conducted the actual uncertainty analysis using estimates for the year 1995, because it was the base
year (i.e., year of key input data collection) of the emissions and activity data estimates for many
emission sources. In the 2011 GHGI, the above seven sources contributed nearly 94% of the total 1995
methane emissions from petroleum systems. To define the uncertainty model parameters (steps 1 and 2
in Figure 1) of every element of the activity and emission factors for the top seven sources, EPA
combined judgments of an industry expert and a statistical expert along with data published in the 1999
EPA-Radian study. For all top seven sources, EPA assumed a lognormal PDF as default (except for oil
tanks, for which EPA assumed a combination of normal and triangular distributions to represent inputs).
Then, using the Monte Carlo simulation method in @RISK (steps 3 through 5 in Figure 1), EPA calculated
the upper and lower estimates representing the 95% confidence interval for each of the top seven
sources.
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June 2017
Table 2. Top Seven Emission Sources for Petroleum Systems in Previous (2011) Uncertainty Analysis
for GHG Inventory Published in 2011 (2011 GHGI)

2011 GHGI CH4

Emissions, year 1995
Source
(MMT CChe)
Shallow Water Offshore Platforms (production segment)
16.1
High-Bleed Pneumatic Controllers (production segment)
9.0
Oil Tanks (production segment)
5.6
Low-bleed Pneumatic Controllers (production segment)
2.6
Gas Engines (production segment)
2.0
Chemical Injection Pumps (production segment)
1.3
Deep Water Offshore Platforms (production segment)
0.4
Other Emission Sources
2.6
Total Emissions from Petroleum Systems
39.7
For petroleum systems, the 2011 analysis assumed that uncertainty for these top seven emissions
sources is an indication of uncertainty for the remaining emissions sources, and therefore extended the
uncertainty of aggregate emissions estimates for the top seven emissions sources to the remaining
sources. With that assumption, the overall uncertainty combining the top seven sources and remaining
sources was re-estimated using the @RISK model.
To develop the uncertainty bounds for 1995, the upper and lower modeled estimates for the source
category were translated to +/- percentages of the GHGI estimate. EPA calculated that for 1995, the 95%
confidence interval for petroleum systems emissions is -24% and +149% of the GHGI-reported value.
These +/- percentage values were assumed to represent the 95% confidence interval for all other years
of the time series.
Updated Uncertainty Analyses for Natural Gas and Petroleum Systems in the 2018 GHGI
In recent years, EPA has made significant revisions to the GHGI methodology to use updated activity and
emissions data in calculating estimates for recent years of the time series. For the 2016 and 2017 GHGIs,
EPA used multiple recently published studies as well as GHGRP Subpart W data to revise the emission
factors and activity data for many natural gas systems emission sources and petroleum systems
production segment emission sources. To update its characterization of uncertainty, EPA has conducted
a draft quantitative uncertainty analysis similar to that conducted for the 2011 GHGI using the IPCC-
recommended Approach 2 methodology (Monte Carlo Simulation technique).
Approach
For its updated analysis, as in the 2011 GHGI analysis, EPA first identified a select number of "top"
emission sources for each source category. Table 3 and Table 4 show the top emission sources that
cover at least 75% of gross emissions in natural gas and petroleum systems for year 2015, respectively,
based on the 2017 GHGI. The top 14 natural gas systems sources cover approximately 77% of total
source category emissions for the year 2015; the top 5 petroleum systems sources cover 79% of total
source category emissions for the year 2015. EPA seeks stakeholder feedback on how many top
emission sources to include in the detailed uncertainty analysis for each source category (see next
section).
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June 2017
Table 3. Top 14 Natural Gas Systems CH4 Emission Sources in the 2017 GHGI
Emission Source (segment)
Year 2015 Gross Emissions
(MMT CO; Eq.)
% of Source
Category Emissions
G&B stations (production)
49.2
27%
Pneumatic controllers (production)
25.5
14%
Station total fugitives (transmission)
14.3
8%
Engine combustion (transmission)
6.3
3%
Engine combustion (production)
6.3
3%
Engine combustion (processing)
5.8
3%
Liquids unloading (production)
5.2
3%
G&B episodic events (production)
4.9
3%
Pipeline venting (transmission and storage)
4.6
3%
G&B pipeline leaks (production)
4.0
2%
Station venting (transmission)
3.8
2%
Shallow water offshore platforms (production)
3.1
2%
Chemical injection pump venting (production)
3.0
2%
Separator fugitives (production)
2.9
2%
Subtotal, Top Sources
139.1
77%
Natural Gas Systems Total
181.1
100%
Table 4. Top 5 Petroleum Systems CH4 Emission Sources in the 2017 GHGI
Emission Source (segment)
Year 2015 Gross Emissions
(MMT CO; Eq.)
% of Source
Category Emissions
Pneumatic controllers (production)
18.6
48%
Shallow water offshore platforms (production)
4.2
11%
Associated gas venting and flaring (production)
3.7
9%
Engine combustion (production)
2.3
6%
Oil tanks (production)
2.0
5%
Subtotal, Top Sources
30.8
79%
Petroleum Systems Total
39.0
100%
Next, EPA developed uncertainty model parameters based on published studies, GHGRP Subpart W
data, expert consultation, and/or the 2011 uncertainty analysis for each of the top emission sources.
Appendix A documents the uncertainty parameters values for the top sources in natural gas and
petroleum systems used in conducting the Monte Carlo analysis, including:
•	Basis of the GHGI value,
•	Basis of the uncertainty parameter values,
•	PDF,
•	Point estimate (i.e., estimate in GHGI which is modeled as the mean or most likely value), and
•	Uncertainty range (e.g., standard deviation or minimum and maximum).
If the modeling input (e.g., emission factor) was based on GHGRP subpart W data, EPA's general
approach was to employ bootstrapping to determine the shape and other parameters of the sampling
distribution of the mean value. The bootstrapping analysis enabled the determination of the PDF (e.g.,
normal, lognormal, triangular, etc.) as well as applicable statistical parameters (e.g., standard deviation,
maximum, minimum, etc.) needed for the Monte Carlo simulation. Most model inputs from GHGRP
were determined to have a normal PDF as expected due to the Central Limit Theorem.2 For modeling
2 GHGRP subpart W data sets contain information (e.g., methane emissions, number of pneumatic controllers, etc.)
submitted by hundreds of facilities which generally include the majority of activity in each industry segment (e.g.,
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June 2017
inputs based on recently published studies (i.e., Marchese et al. and Zimmerle et al.), uncertainty
information available in the study were directly used for the EPA's analyses.3 For modeling inputs based
on older data sets (e.g., EPA/GRI study) or "macro parameters/' that are used as inputs to several
emission source estimates (e.g., total active well count), EPA generally treated the input as a point
estimate and referred to published estimates, the previous uncertainty analysis, and expert judgment to
estimate upper and lower bounds. For input values obtained from certain data sources—for example,
EIA or Drillinglnfo—EPA assigned default uncertainty bounds as documented in the Appendix A tables;
EPA specifically seeks feedback on these default bounds in the next section.
Results
Table 5 and Table 6 below summarize calculated source category level uncertainty estimates for natural
gas and petroleum systems, respectively, based on year 2015 emissions from the 2017 GHGI.
natural gas production). Hence, the bootstrap samples drawn from these GHGRP subpart W data sets were
sufficiently large for the purposes of the CLT.
3 In most cases, the needed uncertainty information (e.g., standard deviation, PDF type) had to be statistically
imputed using information provided in the study. For example, when a study only reported 90% confidence
bounds and the shape of the PDF, the standard deviation of the distribution needed for the Monte Carlo analysis
had to be back-calculated.
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June 2017
Table 5. Summary of Natural Gas Systems Year 2015 CH4 Uncertainty Draft Update Results
Emission Source
Mean Year
2015 Emissions
(MT CO? Eq.)
2.5% Lower Bound of Mean
Year 2015 Emissions
(MT CO? Eq.)
97.5% Upper Bound of Mean Year
2015 Emissions
(MT CO? Eq.)
Value
%
Value
%
G&B Stations (Production)

49,192,568
44,624,214
-9%
53,751,668
9%
Pneumatic Controllers (Production)
High-bleed Pneumatic Controllers
2,368,036
1,127,456
-52%
3,976,192
68%
Intermittent-bleed Pneumatic Controllers
22,380,215
12,745,236
-43%
33,762,516
51%
Low-bleed Pneumatic Controllers
757,911
123,611
-84%
1,600,761
111%
Subtotal
25,506,161
13,996,303
-45%
39,339,469
54%
Station Total Fugitives (Transmission)
Station, Incl. Compressor Components
2,934,282
2,998,572
2%
5,893,251
101%
Reciprocating Compressors
8,484,047
8,113,652
-4%
18,712,780
121%
Centrifugal Compressor (Wet Seals)
1,424,742
1,330,850
-7%
3,181,959
123%
Centrifugal Compressor (Dry Seals)
1,467,867
1,364,200
-7%
3,289,167
124%
Subtotal
14,310,937
13,807,274
-4%
31,077,158
117%
Engine Combustion (Production)
6,323,058
451,872
-93%
22,799,143
261%
Engine Combustion (Transmission)
6,299,036
2,107,162
-67%
8,312,883
32%
Engine Combustion (Processing)
5,806,032
1,961,980
-66%
7,381,227
27%
G&B Episodic Events (Production)
4,879,055
190,554
-96%
25,996,939
433%
Pipeline Venting (Transmission and Storage)
4,590,999
1,213,464
-74%
6,321,547
38%
G&B Pipeline Leaks (Production)
4,038,975
1,448,235
-64%
7,190,027
78%
Station Venting (Transmission)
3,849,139
1,072,316
-72%
10,306,627
168%
Chemical Injection Pump Venting (Production)
3,034,943
2,000,869
-34%
4,109,285
35%
Liquids Unloading With Plunger Lift (Production)
3,016,831
2,521,706
-16%
3,535,875
17%
Liquids Unloading Without Plunger Lift (Production)
2,211,607
1,681,552
-24%
2,739,094
24%
Shallow Water Offshore Platforms (Production)
3,086,499
452,253
-85%
5,698,836
85%
Separator Fugitives (Production)
2,924,891
1,738,168
-41%
4,201,238
44%
Total for Sources Modeled in Uncertainty Assessment
139,070,729
89,267,920
-36%
232,761,014
+67%
Total for Sources Not Modeled in Uncertainty Assessment
23,354,602
14,991,053
-36%
39,088,317
+67%
Source Category Total
162,425,331
104,258,973
-36%
271,849,330
+67%
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June 2017
Table 6. Summary of Petroleum Systems Year 2015 CH4 Uncertainty Draft Update Results
Emission Source
Mean Year 2015
Emissions
(MT C02 Eq.)
2.5% Lower Bound of Mean Year
2015 Emissions
(MT C02 Eq.)
97.5% Upper Bound of Mean Year
2015 Emissions
(MT C02 Eq.)
Value
%
Value
%
Pneumatic Controllers
(Production)
High-bleed Pneumatic Controllers
2,126,086
635,320
-70%
4,175,097
96%
Intermittent-bleed Pneumatic Controllers
15,887,354
7,674,488
-52%
26,842,275
69%
Low-bleed Pneumatic Controllers
619,806
79,695
-87%
1,436,872
132%
Subtotal
18,633,247
8,389,503
-55%
32,454,244
74%
Shallow Water Oil Platforms
(Production)
Shallow Water Oil Platforms
4,207,887
1,052,724
-75%
11,578,951
175%
Subtotal
4,207,887
1,052,724
-75%
11,578,951
175%
Associated Gas Flaring &
Venting (Production)
Associated Gas Flaring
2,642,647
1,180,379
-55%
4,430,285
68%
Associated Gas Venting
1,062,962
79,843
-92%
2,623,880
147%
Subtotal
3,705,610
1,260,222
-66%
7,054,166
90%
Oil Tanks (Production)
Large Oil Tanks with Flares
202,495
83,698
-59%
337,425
67%
Large Oil Tanks with VRU
99,012
13,183
-87%
206,330
108%
Large Oil Tanks without Controls
1,443,504
595,372
-59%
2,475,665
72%
Small Oil Tanks with Flares
1,726
277
-84%
4,972
188%
Small Oil Tanks without Controls
115,514
4,920
-96%
370,501
221%
Large Oil Tank Separators with
Malfunctioning Dump Valves
149,605
22,046
-85%
524,525
251%
Subtotal
2,011,857
719,495
-64%
3,919,418
95%
Gas Engine Combustion
(Production)
Gas Engine Combustion
2,254,932
33,122
-99%
7,195,582
219%
Subtotal
2,254,932
33,122
-99%
7,195,582
219%
Total for Sources Modeled in Uncertainty Assessment
30,813,532
11,455,066
-63%
62,202,361
+102%
Total for Sources Not Modeled in Uncertainty Assessment
9,062,042
3,368,854
-63%
18,293,274
+102%
Source Category Total
39,875,574
14,823,920
-63%
80,495,635
+102%
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June 2017
Requests for Stakeholder Feedback
EPA seeks stakeholder feedback on the following considerations in developing an uncertainty analysis
for the 2018 GHGI:
1.	The following elements of EPA's general approach to uncertainty analysis:
a.	Performing a detailed uncertainty analysis for "top" sources that cover a specified percent (e.g.,
75%) of gross emissions in natural gas (Table 3) and petroleum systems (Table 4) for year 2015,
and extending the uncertainty of aggregate emissions estimates for the top emissions sources to
the remaining sources (as illustrated in Table 5 and Table 6).
b.	Calculating uncertainty for a select year, then assuming the same relative uncertainty as the
95% confidence interval for all other years of the time series.
2.	The availability of additional information and data from statistical and industry experts that are
relevant to characterizing the uncertainty parameters for the sources listed in Table 3 and Table 4
and detailed in Appendix A.
3.	How to compare estimated uncertainty ranges from different studies and measurement/calculation
approaches, and important caveats and considerations. Appendix B of this memorandum compares
the GHGI uncertainty ranges to uncertainty characterizations presented in several recently
published studies. Which other studies have information on uncertainty that could be compared
with the GHGI uncertainty ranges?
4.	The uncertainty ranges in the GHGI reflect the uncertainty in the available emissions and activity
data. Any systematic errors that may arise because of imperfections in conceptualization, models,
measurement techniques, or other systems for recording or making inferences from data are not
reflected in the uncertainty analysis because we lack information on them. Such errors, if they exist,
could bias the results leading to either under- or over-estimates. The EPA requests additional
information to characterize systematic errors in the GHGI, how and where these could be described
in the GHGI, and how they could be incorporated into the uncertainty analysis.
5.	Additional steps that could be taken to improve characterization of the PDFs. Most model inputs for
this uncertainty assessment were determined from our analysis to have a normal PDF, as expected
due to the Central Limit Theorem (CLT). As discussed earlier in this memo, the CLT states that the
means of random samples drawn from a population with any type of distribution will be normally or
near-normally distributed if the sample size is large enough (Mendenhall, Wackerly, & Scheaffer,
1990). The EPA seeks feedback on general approaches to consider for data sources for which sample
sizes are comparatively small; the sampling methodology could be biased (e.g., may not result in a
nationally representative sample); or only certain statistical parameters (e.g., mean and standard
deviation) rather than the full underlying dataset were available in the source material.
6.	While they do not provide overall uncertainty estimates for CH4 emissions from natural gas systems,
Brandt et al. (2016) argue that uncertainty ranges in the previous GHG Inventories might be too
narrow for some source categories due to existence of extreme distributions in natural gas data
sets.4 EPA seeks feedback on approaches that would improve characterization of extreme
distributions for the GHGI uncertainty analysis.
4 More specifically, they assert that "...(1) heavy-tailed distributions are a pervasive characteristic of natural gas
leak size distributions; (2) natural gas leaks are more heavy-tailed than other natural and social phenomena, (3) the
largest 5% of leaks are (by median expectation) responsible for over 50% of the leaked methane from a given
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7.	As shown in tables A1 and A2, EPA has assigned default uncertainty bounds for point estimates
obtained from certain data sources. EPA seeks feedback on these values:
a.	National estimates of gas production, gas consumption, and oil production (EIA): +/-1%
b.	National estimate of transmission pipeline miles (PHMSA): +/-1%
c.	National well count estimates (developed by EPA from Drillinglnfo data): +/- 5%
8.	How improved uncertainty results can be used to target improvements for the GHGI.
source category; (4) the recent use of log-normal distributions to model the distribution of leaks within a source
category is not supported and systematically underestimates the importance of large emitters; (5) heavier-than-
log-normal distributions lead to larger uncertainty than currently included in official estimates; (6) robustly
characterizing heavy-tailed distributions will require sample sizes much larger than currently used in most studies;
(7) aggregating results across studies to improve accuracy and robustness is statistically challenging."
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Appendix A: Uncertainty Parameter Values for the Top Sources in Natural Gas and Petroleum Systems Used in Conducting Monte Carlo Analysis
As described above, the national emissions estimate associated with a source category is computed as the product of the average emission factor and
average activity factor for that source category. Thus, the uncertainty parameters presented in Tables A1 and A2 below are associated with the
distribution of these average values; not the distribution of emissions from that source (for more information, see section "Updated Uncertainty
Analyses for Natural Gas and Petroleum Systems in the 2018 GHGI: Approach" above).
Table Al. Overview of Natural Gas Systems Year 2015 CH4 Uncertainty Inputs for @RISK Modeling
Emissions Calculation Input
Input Basis"
Uncertainty Basis
PDF
Mean or Point
Estimate or
Most Likely
Value
Standard
Deviation ''
Lower Bound
or Minimum
Upper Bound
or Maximum
Macro Parameters
National active gas well count,
2015
Drillinglnfo
Expert Judgment, 5%
Uniform
421,893
-
400,798
442,988
Methane content of natural gas for
NE region
GTI (2001), EIA
Statistical analysis of Allen et al. (2013)
methane content data
Normal
0.865
0.008
-
-
Methane content of natural gas for
MC region
GTI (2001), EIA
Statistical analysis of Allen et al. (2013)
methane content data
Normal
0.824
0.030
-
-
Methane content of natural gas for
RM region
GTI (2001), EIA
Statistical analysis of Allen et al. (2013)
methane content data
Normal
0.774
0.006
-
-
Methane content of natural gas for
SW region
GTI (2001), EIA
Statistical analysis of Allen et al. (2013)
methane content data
Normal
0.805
0.013
-
-
Methane content of natural gas for
WC region
GTI (2001), EIA
Statistical analysis of Allen et al. (2013)
methane content data
Normal
0.919
0.014
-
-
Methane content of natural gas for
GC region
GTI (2001), EIA
Statistical analysis of Allen et al. (2013)
methane content data
Normal
0.888
0.019
-
-
Default methane content of
natural gas
EPA/GRI (1996)
Statistical analysis of Allen et al. (2013)
methane content data
Normal
0.788
0.008
-
-
Gas Wells for NE Region (2015)
Drillinglnfo
Expert Judgment, 5%
Uniform
153,380
-
145,711
161,049
Gas Wells for MC Region (2015)
Drillinglnfo
Expert Judgment, 5%
Uniform
79,645
-
75,663
83,627
Gas Wells for RM Region (2015)
Drillinglnfo
Expert Judgment, 5%
Uniform
75,689
-
71,905
79,473
Gas Wells for SW Region (2015)
Drillinglnfo
Expert Judgment, 5%
Uniform
45,370
-
43,102
47,639
Gas Wells for WC Region (2015)
Drillinglnfo
Expert Judgment, 5%
Uniform
2,417
-
2,296
2,538
Gas Wells for GC Region (2015)
Drillinglnfo
Expert Judgment, 5%
Uniform
65,392
-
62,122
68,662
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Emissions Calculation Input
Input Basis"
Uncertainty Basis
PDF
Mean or Point
Estimate or
Most Likely
Value
Standard
Deviation ''
Lower Bound
or Minimum
Upper Bound
or Maximum
G&B Stations (Production)
scfd/station
Marchese, et al.
(2015)
Statistical analysis of study data
Normal
53,066
2,468


Region Marketed Onshore
Production (MMCF), 2015
EIA
EIA publication default, 1%
Uniform
7,499,108
-
7,424,117
7,574,099
National Marketed Onshore
Production (BCF), 2012
EIA
EIA publication default, 1%
Uniform
23,531
-
23,295
23,766
NE Region Marketed Onshore
Production (MMCF), 2015
EIA
EIA publication default, 1%
Uniform
4,443,949
-
4,399,509
4,488,388
MC Region Marketed Onshore
Production (MMCF), 2015
EIA
EIA publication default, 1%
Uniform
5,087,452
-
5,036,577
5,138,326
RM Region Marketed Onshore
Production (MMCF), 2015
EIA
EIA publication default, 1%
Uniform
2,177,308
-
2,155,535
2,199,081
WC Region Marketed Onshore
Production (MMCF), 2015
EIA
EIA publication default, 1%
Uniform
521,702
-
516,485
526,919
GC Region Marketed Onshore
Production (MMCF), 2015
EIA
EIA publication default, 1%
Uniform
7,554,759
-
7,479,211
7,630,306
Pneumatic Controllers
(Production)
scfd/controller (low bleed)
Subpart WRY2015
& EPA/GRI (1996)
Statistical analysis of reported Subpart W
data; PDF per expert judgment; statistical
parameters for emission rate imputed using
the reported 90% confidence bound in
EPA/GRI study
Normal
23
10
-
-
scfd/controller (high bleed)
Subpart WRY2015
& EPA/GRI (1996)
Statistical analysis of reported Subpart W
data; PDF per expert judgment; statistical
parameters for emission rate imputed using
the reported 90% confidence bound in
EPA/GRI study
Normal
622
100
-
-
scfd/controller (intermittent
bleed)
Subpart WRY2015
& EPA/GRI (1996)
Statistical analysis of reported Subpart W
data; PDF per expert judgment; statistical
parameters for emission rate imputed using
the reported 90% confidence bound in
EPA/GRI study
Normal
218
42
-
-
Fraction of total controllers that
are low bleed
Subpart WRY2015
Statistical analysis of reported data
Normal
0.24
0.05
-
-
Fraction of total controllers that
are high bleed
Subpart WRY2015
Statistical analysis of reported data
Normal
0.03
0.01
-
-
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Emissions Calculation Input
Input Basis"
Uncertainty Basis
PDF
Mean or Point
Estimate or
Most Likely
Value
Standard
Deviation ''
Lower Bound
or Minimum
Upper Bound
or Maximum
Fraction of total controllers that
are intermittent bleed
Subpart WRY2015
Statistical analysis of reported data
Normal
0.73
0.05
-
-
Total controllers per well
Subpart WRY2015
Statistical analysis of reported data
Normal
1.88
0.23
-
-
Station Total Fugitives
(Transmission)
scfd/station
Zimmerle, et al.
(2015)
PDF per expert judgment; statistical
parameters imputed using the reported 95%
confidence bound
Normal
9,104
1,269


Station scaling factor for national
count based on subpart W count
Zimmerle, et al.
(2015); Subpart W
RY2012
Statistical analysis of study data
Lognormal
3.52
0.5085
1.5855
(Shift)d
-
Engine Combustion
(Transmission)
scf/HPhr
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Triangular
0.240
-
0.045
0.323
1992 MMHPhr
EPA/GRI (1996)
PDF per expert judgment; statistical
parameters imputed using the reported 90%
confidence bound
Normal
40,380
4,194
-
-
2015 Total National gas
Consumption (tril ftA3 / yr)
EIA
EIA publication default, 1%
Uniform
27
-
27
28
1992 Total National gas
Consumption (tril ftA3 / yr)
EIA
EIA publication default, 1%
Uniform
20
-
20
20
Engine Combustion (Production)
scf/HPhr
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Triangular
0.240
-
0.045
0.323
MMHPhr (for All gas wells in 1992)
EPA/GRI (1996)
PDF per expert judgment; statistical
parameters imputed using the reported 90%
confidence bound
Lognormal
27,460
32,531


Total Gas Wells (2015) (excluded
NE)
Drillinglnfo
Expert Judgment, 5%
Uniform
268,513
-
255,087
281,939
Total Gas Wells (1992) (excluded
NE)
Drillinglnfo
Expert Judgment, 5%
Uniform
140,758
-
133,720
147,796
Engine Combustion (Processing)
scf/HPhr
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Triangular
0.240
-
0.045
0.323
MMHPhr/plant
Subpart WRY2015
Statistical analysis of reported data
Normal
75.3
4.9
-
-
2015 national plant count
O&GJ (2014)
O&GJ publication default, 1%
Uniform
667
-
660
674
Liquids Unloading (Production)
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Emissions Calculation Input
Input Basis"
Uncertainty Basis
PDF
Mean or Point
Estimate or
Most Likely
Value
Standard
Deviation ''
Lower Bound
or Minimum
Upper Bound
or Maximum
Fraction of wells that vent using
plungers
Subpart WRY2015
Statistical analysis of reported data
Normal
0.100
0.008
-
-
Fraction of wells that vent without
using plungers
Subpart WRY2015
Statistical analysis of reported data
Normal
0.068
0.008
-
-
scfy/plunger well
Subpart WRY2015
Statistical analysis of reported data
Normal
148,589
966
-
-
scfy/non-plunger well
Subpart WRY2015
Statistical analysis of reported data
Normal
160,411
562
-
-
G&B Episodic Events (Production)
Total CH4 Emissions from G&B
Episodic Events (Gg/yr), 2012
Marchese, et al.
(2015)
PDF per expert judgment; statistical
parameters imputed using the reported 95%
confidence bound reported for 2012 estimate
Lognormal
169
330
-
-
Marketed Onshore Production
(MMCF), 2015
EIA
EIA publication default, 1%
Uniform
27,284
-
27,011
27,557
National Marketed Onshore
Production (BCF), 2012
EIA
EIA publication default, 1%
Uniform
23,531
-
23,295
23,766
Pipeline Venting (Transmission
and Storage)
Mscfy/mile
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Triangular
31.7
-
2.7
47.8
Transmission Pipeline Miles
PHMSA (2015)
PHMSA publication default, 1%
Uniform
301,748
-
298,731
304,765
G&B Pipeline Leaks (Production)
miles/well for NE Region
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
0.400
0.071
-
-
miles/well for MC Region
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
0.620
0.110
-
-
miles/well for RM Region
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
1.120
0.194
-
-
miles/well for SW Region
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
1.120
0.195
-
-
miles/well for WC Region
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
1.120
0.195
-
-
miles/well for GC Region
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
1.120
0.198
-
-
miles (equally divided for each
region)
EPA/GRI (1996)
PDF and mileage variability per expert
judgment; statistical analysis of study data
Normal
14,367
3,668
-
-
CH4 emissions, Bcf
EPA/GRI (1996)
Buildup of the four pipeline materials using
Monte Carlo sampling informed by the 90%
confidence intervals reported in the GRI 1996
documentation.
Normal
6.6
2.1
-
-
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Emissions Calculation Input
Input Basis"
Uncertainty Basis
PDF
Mean or Point
Estimate or
Most Likely
Value
Standard
Deviation ''
Lower Bound
or Minimum
Upper Bound
or Maximum
miles
EPA/GRI (1996)
PDF per expert judgment; statistical
parameters imputed using the reported 90%
confidence bound reported
Normal
340,200
20,700
-
-
Station Venting (Transmission)
mscfy/station
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
4,359
1,787
-
-
Station scaling factor for national
count based on subpart W count
Zimmerle, et al.
(2015); Subpart W
RY2012
Statistical analysis of study data
Lognormal
3.52
0.5085
1.5855
(Shift)d
-
Chemical Injection Pump Venting
(Production)
# pumps/well
Subpart WRY2015
Statistical analysis of reported data
Normal
0.189
0.033
-
-
scfd/pump
Subpart WRY2015
& EPA/GRI (1996)
Statistical analysis of reported Subpart W
data; PDF per expert judgment; statistical
parameters for emission rate imputed using
the reported 90% confidence bound in
EPA/GRI study
Normal
216.4
6.7
-
-
Shallow Water Offshore Platforms
(Production)
scfd/platform
EPA (2015)
Statistical parameters imputed using the
reported PDF and 90% confidence bound in
2000 GOADS analysis (EPA (2005))
Normal
8,899
3,873
-
-
Shallow Water Gas Platforms
BOEM (2011) &
EPA (2008)
Statistical parameters imputed using the
reported standard deviation in 2011
uncertainty analysis (EPA (2010a))
Normal
1,973
17
-
-
Separator Fugitives (Production)
# separators/well
Subpart WRY2015
Statistical analysis of reported data
Normal
0.685
0.045
-
-
scfd/separator (NE & MC Region)
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
0.899
0.045
-
-
scfd/separator (except NE & MC
Region)
EPA/GRI (1996)
PDF per expert judgment; statistical analysis of
study data
Normal
122.016
24.439
-
-
indicates not applicable
a - Refer to the Natural Gas Systems 2018 annex tables (https://www.epa.gov/ghgemissions/additional-information-oil-and-gas-estimates-1990-2015-ghg-inventory-published-april)
for more detailed documentation of the estimate basis,
b - Applicable for "mean" input values (normal PDF).
c - Lower and upper bounds are applicable for "point estimate" input values (uniform PDF). Minimum and maximum values are applicable for "most likely" input values (triangular
PDF).
d - This is not a lower bound or minimum, rather, the lognormal function for this particular input has a shift parameter.
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Table A2. Overview of Petroleum Systems Year 2015 CH4 Uncertainty Inputs for @RISK Modeling
Emissions Calculation Input
Input Basis"
Uncertainty Basis
PDF
Mean or Point
Estimate or
Most Likely
Value
Standard
Deviation ''
Lower Bound
or Minimum
Upper Bound
or Maximum'
Macro Parameters
National active oil well count
Drillinglnfo
Expert Judgment, 5%
Uniform
586,896
-
557,551
616,241
Pneumatic Controllers
(Production)
scfd/controller (high bleed)
Subpart WRY2015
& EPA/GRI (1996)
Statistical analysis of reported Subpart W
data; PDF per expert judgment; statistical
parameters for emission rate imputed using
the reported 90% confidence bound in
EPA/GRI study
Normal
621.73
100.20
-
-
Fraction of total controllers that
are high bleed
Subpart WRY2015
Statistical analysis of reported data
Normal
0.03
0.01
-
-
scfd/controller (intermittent
bleed)
Subpart WRY2015
& EPA/GRI (1996)
Statistical analysis of reported Subpart W
data; PDF per expert judgment; statistical
parameters for emission rate imputed using
the reported 90% confidence bound in
EPA/GRI study
Normal
218.32
41.90
-
-
Fraction of total controllers that
are intermittent bleed
Subpart WRY2015
Statistical analysis of reported data
Normal
0.70
0.07
-
-
scfd/controller (low bleed)
Subpart WRY2015
& EPA/GRI (1996)
Statistical analysis of reported Subpart W
data; PDF per expert judgment; statistical
parameters for emission rate imputed using
the reported 90% confidence bound in
EPA/GRI study
Normal
22.85
9.51
-
-
Fraction of total controllers that
are low bleed
Subpart WRY2015
Statistical analysis of reported data
Normal
0.26
0.07
-
-
Total controllers per well
Subpart WRY2015
Statistical analysis of reported data
Normal
1.002
0.195
-
-
Shallow Water Offshore
Platforms (Production)
scfd CH4/platform
EPA (2015)
Statistical parameters imputed using the
reported PDF and 90% confidence bound in
2000 GOADS analysis (EPA (2005))
Lognormal
16,552
11,146
-
-
Total number shallow water GOM
platforms
BOEM (2011) &
EPA (2008)
Statistical parameters imputed using the
reported standard deviation in 2011
uncertainty analysis (EPA (2010b))
Normal
1,447
10.45
-
-
Oil Tanks (Production)
scf/bbl (large with flare)
Subpart WRY2015
Statistical analysis of reported data
Normal
0.35
0.10
-
-
throughput fraction (large with
flare)
Subpart WRY2015
Statistical analysis of reported data
Normal
0.55
0.05
-
-
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Emissions Calculation Input
scf/bbl (large with VRU)
Input Basis"
Subpart WRY2015
Uncertainty Basis
Statistical analysis of reported data
PDF
Normal
Mean or Point
Estimate or
Most Likely
Value
0.47
Standard
Deviation ''
0.21
Lower Bound
or Minimum
Upper Bound
or Maximum'
throughput fraction (large with
VRU)
Subpart WRY2015
Statistical analysis of reported data
Normal
0.201
0.04
-
-
scf/bbl (large uncontrolled)
Subpart WRY2015
Statistical analysis of reported data
Normal
7.90
2.18
-
-
throughput fraction (large
uncontrolled)
Subpart WRY2015
Statistical analysis of reported data
Normal
0.18
0.03
-
-
scf/bbl (small with flare)
Subpart WRY2015
Statistical analysis of reported data
Lognormal
0.088
0.050
-
-
throughput fraction (small with
flare)
Subpart WRY2015
Statistical analysis of reported data
Normal
0.019
0.007
-
-
scf/bbl (small uncontrolled)
Subpart WRY2015
Statistical analysis of reported data
Lognormal
2.3
1.4
-
-
throughput fraction (small
uncontrolled)
Subpart WRY2015
Statistical analysis of reported data
Normal
0.05
0.02
-
-
scf/bbl (malfunctioning dump
valves)
Subpart WRY2015
Statistical analysis of reported data
Lognormal
0.15
0.15
-
-
throughput fraction
(malfunctioning dump valves)
Subpart WRY2015
Statistical analysis of reported data
Normal
0.932
0.025
-
-
% National throughput managed
by tanks
Subpart WRY2015
Statistical analysis of reported data
Normal
0.63
0.07
-
-
National oil production
EIA
EIA publication default, 1%
Uniform
3.44E+09
-
3.41E+09
3.48E+09
Associated Gas Venting and
Flaring (Production)
Fraction of total wells with venting
or flaring
Subpart WRY2015
Statistical analysis of reported data
Normal
0.12
0.02
-
-
Fraction of wells with associated
gas that flare
Subpart WRY2015
Statistical analysis of reported data
Normal
0.83
0.06
-
-
mscfy/flaring well
Subpart WRY2015
Statistical analysis of reported data
Normal
95
21
-
-
fraction of wells with associated
gas that vent
Subpart WRY2015
Statistical analysis of reported data
Normal
0.17
0.06
-
-
mscfy/venting well
Subpart WRY2015
Statistical analysis of reported data
Normal
193
94
-
-
Gas Engine Combusion
(Production)
scf/HPhr
EPA/GRI (1996)
Statistical analysis of GRI data (NETL, 2016)
Triangular
0.24
-
0.04
0.32
compressors
EPA/Radian (1999)
PDF per expert judgment; statistical
parameters imputed using the reported 90%
confidence bound calculated for 1993
estimate
Normal
3,097
1,522
-
-
MMhp-hr/compressor
EPA/Radian (1999)
PDF per expert judgment; statistical
parameters imputed using the reported 90%
Lognormal
6.30
4.95
-
-
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Emissions Calculation Input
Input Basis"
Uncertainty Basis
PDF
Mean or Point
Estimate or
Most Likely
Value
Standard
Deviation ''
Lower Bound
or Minimum
Upper Bound
or Maximum'


confidence bound calculated for 1993
estimate





indicates not applicable
a - Refer to the Petroleum Systems 2017 annex tables (https://www.epa.gov/ghgemissions/additional-information-oil-and-gas-estimates-1990-2015-ghg-inventory-published-april)
for more detailed documentation of the estimate basis,
b - Applicable for "mean" input values (normal PDF).
c - Lower and upper bounds are applicable for "point estimate" input values (uniform PDF). Minimum and maximum values are applicable for "most likely" input values (triangular
PDF).
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Appendix A: References for Tables A1 and A2
Allen et al. (2013) Measurements of methane emissions at natural gas production sites in the United
States. Proc. Natl. Acad. Sci. U.S.A. 110, 17768-17773 (2013)
Drillinglnfo Dl Desktop® Raw Data PLUS Download. Drillinglnfo. Inc. April 2016.
EIA (various published data sets). Energy Information Administration, U.S. Department of Energy.
Washington, DC.
EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Radian. U.S.
Environmental Protection Agency. April 1996.
EPA/Radian (1999) Methane Emissions from the U.S. Petroleum Industry. Prepared by Radian
International. U.S. Environmental Protection Agency. February 1999.
GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases.
Second Edition. GRI- 01/0136.
OGJ (2014) "Worldwide Gas Processing." Oil & Gas Journal, PennWell Corporation, Tulsa, OK. 2014.
Marchese et al. (2015) Methane Emissions from United States Natural Gas Gathering and Processing.
Environmental Science & Technology, 49, 10718-10727. 2015.
PHMSA (2015) Transmission Annuals Data. Pipeline and Hazardous Materials Safety Administration, U.S.
Department of Transportation, Washington, DC.
BOEM (2011) Platform Information and Data. Bureau of Ocean Energy Management, U.S. Department of
Interior. 2011. https://www.data.boem.gov/homepg/data center/index.asp.
EPA (2005) Incorporating the Mineral Management Service Gulfwide Offshore Activities Data System
(GOADS) 2000 data into the methane emissions inventories. Prepared by ICF International. U.S.
Environmental Protection Agency. Exhibit 6, page 10. June 30, 2005.
EPA (2008) Natural Gas Model Activity Factor Basis Change. Prepared by ICF International. U.S.
Environmental Protection Agency. January 7, 2008.
EPA (2010a) Natural Gas Uncertainty Model 11-30-10. Excel Spreadsheet. Prepared by ICF International.
U.S. Environmental Protection Agency. Table 8. November 30, 2010.
EPA (2010b) 2009 Petroleum Uncertainty Model 11-30-10. Excel Spreadsheet. Prepared by ICF
International. U.S. Environmental Protection Agency. Table 8. November 30, 2010.
EPA (2015) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Offshore Oil
and Gas Platforms Emissions Estimate. Table 1, page 2. April 2015. Available online at:
http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/natural-gas-systems.html.
Zimmerle, et al. (2015) Methane Emissions from the Natural Gas Transmission and Storage System in the
United States. Environmental Science and Technology, 49, 9374-9383. 2015.
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Appendix B: Comparison to Recently Published Studies
Large amounts of data and information on natural gas and petroleum systems have recently become
available, through the Greenhouse Gas Reporting Program (GHGRP) and external studies. In general,
there are two major types of studies related to oil and gas GHG data: "bottom up" studies that focus on
measurement or quantification of emissions from specific activities, processes and equipment (e.g.,
GHGRP data), and "top down" studies that focus on verification of estimates (e.g., aircraft and satellite
studies). The first type of study can lead to direct improvements to or verification of GHGI estimates.
The GHG Inventory estimates for oil and gas underwent extensive updates in recent years using data
from these types of studies. The second type of study can provide general indications on potential over-
and under-estimates. EPA reviews both types of studies for data that can inform GHGI updates. In this
section, we compare the updated draft quantitative GHGI uncertainty estimates for CH4 emissions from
natural gas and petroleum systems, using the ranges detailed in this memorandum and developed for
the 2018 GHGI, to those reported in recently published studies that include a bottom up inventory
component (see Tables B1 and B2). While both top down and bottom up studies often include
assessments of uncertainty, a comparison of uncertainty information from studies that use a top down
approach was not developed for this memorandum, and would require further considerations.
All studies reviewed for uncertainty information used Monte Carlo simulation technique to examine
uncertainty bounds for the estimates reported, which is in line with the IPCC recommended Approach 2
methodology. The uncertainty ranges in the studies listed in Tables Bl, B2, and B3 differ from those of
EPA. However, it is difficult to extrapolate uncertainty ranges from these studies to apply to the GHGI
estimates because the GHGI source category level uncertainty analysis is not directly comparable to
source- or segment-specific uncertainty analyses in these studies. Further, the methodologies and data
sources used in estimating CH4 emissions in these studies may differ significantly from the studies
underlying GHGI methodologies.
Table Bl. Comparison of Quantitative Uncertainty Estimates for CH4 Emissions from Natural Gas
	Systems (MMT C02 Eq. and Percent	
Segment
Study
Year
Emissions
(MMT CO;
Eq.)
Uncertainty Rangea
MMT CO; Eq.
%
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
All Segments, National
EPA 2017 GHGI
2015
162.4
104.3
271.9
-36%
67%
Production, Barnett Shale
Lyon, et al. (2015)b
2013
3.6
3.37
3.87
-7%
6%
Gathering Facilities, National
Marchese, et al. (2015)
2012
42.4
37.76
47.09
-11%
11%
Gathering, Barnett Shale
Lyon, et al. (2015)b
2013
4.3
3.00
5.97
-30%
39%
Processing, Barnett Shale
Lyon, et al. (2015)b
2013
1.2
0.81
1.77
-33%
47%
Trans. & Storage, National
Zimmerle, et al. (2015)
2012
37.6
30.44
48.85
-19%
30%
Trans. & Storage, National
Lyon, et al. (2015)b
2013
0.4
0.28
0.55
-28%
39%
Distribution, National
Lamb, et al. (2015)
2013
9.8
NA
21.32
NA
117%
Distribution, Barnett Shale
Lyon, et al. (2015)b
2013
0.2
0.17
0.35
-18%
74%
Oil and Gas, All Segments,
Barnett Shale
Zavala, et al. (2015)c
2013
12.9
10.5
16.0
-19%
24%
All Segments, National
Littlefield, et al. (2017)
2012
183.9
150.3
242.2
-24%
29%
NA = Not available
a The figures represent the 95 percent confidence intervals reported in each of the studies for the source.
b The emission estimates reported are for the 25-county Barnett shale region, not the U.S. as a whole, and
encompass natural gas and petroleum emissions. Therefore, the point estimates are not comparable to those
reported in other studies and are italicized to emphasize such.
c The Zavala et al. results represent both natural gas and petroleum activities.
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June 2017
Table B2. Comparison of Quantitative Uncertainty Estimates for CH4 Emissions from Petroleum
	Systems (MMT C02 Eg. and Percent)	
Segment
Study
Year
Emissions
(MMT CO;
Eq.)
Uncertainty Range"
MMT CO; Eq.
%
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
All Segments, National
EPA 2017 GHGI
2015
39.9
14.8
80.5
-63%
102-'..
Production, Barnett Shale b
Lyon, et al. (2015)
2013
0.39
0.37
0.42
-6%
6%
Oil and Gas, All Segments,
Barnett Shale
Zavala, et al. (2015)c
2013
12.9
10.5
16.0
-19%
24%
a The figures represent the 95 percent confidence intervals reported in each of the studies for the source.
b The emission estimates reported are for the 25-county Barnett shale region, not the U.S. as a whole, and
encompass natural gas and petroleum emissions. Therefore, the point estimates are not comparable to those
reported in other studies and are italicized to emphasize such.
c The Zavala et al. results represent both natural gas and petroleum activities.
Table B3. Comparison of Quantitative Uncertainty Estimates for CH4 Emissions from Specific Emission
	Sources from Natural Gas Systems (MMT C02 Eq. and Percent)	
Segment & Emission
Source
Study
Year
Emissions
(MMT CO;
Eq.)
Uncertainty RangeJ
MMT CO; Eq.
%
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Production, National,
Pneumatic Controllers
EPA 2017 GHGI
2015
25.5
14.0
39.3
-45%
54%
Production, National,
Pneumatic Controllers
Allen, et al. (2014a)
2012
15
9.9
26.3
-34%
75%
Production, National,
Chemical Injection Pump
Venting
EPA 2017 GHGI
2015
3.0
2.0
4.1
-34%
35%
Production, National,
Chemical Injection Pump
Venting
Allen, et al. (2013)
2011
1.7
0.9
2.5
-49%
47%
Production, National,
Liquids Unloading With
Plunger Lifts
EPA 2017 GHGI
2015
3.0
2.5
3.5
-16%
17%
Production, National,
Liquids Unloading Without
Plunger Lifts
EPA 2017 GHGI
2015
2.2
1.7
2.7
-24%
24%
Production, National,
Liquids Unloading With
Plunger Lifts
Allen, et al. (2014b)
2012
4.8
2.8
7.3
-42%
53%
Production, National,
Liquids Unloading Without
Plunger Lifts
Allen, et al. (2014b)
2012
2.0
1.3
4.0
-38%
100%
a The figures represent the 95 percent confidence intervals reported in each of the studies for the source.
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June 2017
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