Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Updates to Natural Gas and Petroleum Systems Uncertainty Estimates

April 2018

Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016:

Updates to Natural Gas and Petroleum Systems Uncertainty Estimates

This memo documents the uncertainty analysis used in EPA's 2018 Inventory of U.S. Greenhouse Gas Emissions
and Sinks (GHGI) as well as historical analyses and considerations. In previous versions of this memo released in
June and October 2017, and during stakeholder webinars and workshops held in April, June, and August 2017,
EPA presented preliminary considerations and sought stakeholder feedback on updating the uncertainty
estimates for natural gas and petroleum systems in the 2018 GHGI.

Before the 2018 GHGI update described in this memo, the most recent uncertainty analysis for the natural gas
and petroleum systems emissions estimates in the 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 2018 GHGI, EPA implemented updates for the natural gas and petroleum systems uncertainty analyses to
reflect new information, current GHGI methodologies, and stakeholder feedback on the uncertainty quantification
methodology. Section 1 of this memo documents the uncertainty modeling approach and results used in EPA's
2018 GHGI; the remainder of the sections contain historical information that was presented in the October 2017
memo, for reference (note that the October 2017 analyses reflect estimates and methodologies used in the 2017
GHGI; therefore, resulting estimates changed for the final 2018 GHGI).

1. 2018 GHGI Uncertainty Analysis

In recent years, EPA has made significant revisions to the Inventory methodology to use updated activity and
emissions data. To update its characterization of uncertainty, EPA has conducted a quantitative uncertainty
analysis using the IPCC Approach 2 methodology (Monte Carlo Simulation technique). EPA used Microsoft Excel's
@RISK add-in tool to estimate the 95 percent confidence bound around CH4 emissions from natural gas systems
and petroleum systems for the 2018 GHGI, then applied the calculated bounds to both CH4 and C02 emissions
estimates.

1.1. Natural Gas Systems Uncertainty

For the natural gas systems analysis, EPA focused on the 16 highest-emitting sources for the year 2016, which
together emitted 78 percent of methane from natural gas systems in 2016, and extrapolated the estimated
uncertainty for the remaining sources. The @RISK add-in provides for the specification of probability density
functions (PDFs) for key variables within a computational structure that mirrors the calculation of the inventory
estimate. The IPCC guidance notes that in using this method, "some uncertainties that are not addressed by
statistical means may exist, including those arising from omissions or double counting, or other conceptual errors,
or from incomplete understanding of the processes that may lead to inaccuracies in estimates developed from
models." The uncertainty bounds reported below only reflect those uncertainties that EPA has been able to
quantify and do not incorporate considerations such as modeling uncertainty, data representativeness,
measurement errors, misreporting or misclassification. The understanding of the uncertainty of emission
estimates for this category evolves and improves as the underlying methodologies and datasets improve.

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emissions in 2016 were estimated to be between 21.5 and 29.9 MMT C02 Eq. at a 95 percent confidence level.
Uncertainty bounds for other years of the time series have not been calculated, but uncertainty is expected to
vary over the time series. For example, years where many emission sources are calculated with interpolated data
would likely have higher uncertainty than years with predominantly year-specific data.

Table 1. 2018 GHGI Quantitative Uncertainty Estimates for CH4 and Non-energy C02 Emissions from
Natural Gas Systems (MMT C02 Eq. and Percent)

Source

Gas

2016 Emissions

Estimate
(MMT CO2 Eq.)

Uncertainty Range Relative to Emissions Estimate3

(MMT CO2 Eq.)

(%)

Lower Boundb

Upper Boundb

Lower Boundb

Upper Boundb

Natural Gas Systems

ch4

163.5

138.0

191.8

-16%

+17%

Natural Gas Systems0

co2

25.5

21.5

29.9

-16%

+17%

a - Range of emission estimates estimated by applying the 95 percent confidence intervals obtained from the Monte Carlo Simulation
analysis conducted for the year 2016 CH4 emissions.

b - All reported values are rounded after calculation. As a result, lower and upper bounds may not be duplicable from other rounded values
in the 2018 GHGI report.

c - An uncertainty analysis for the non-energy C02 emissions was not performed. The relative uncertainty estimated (expressed as a
percent) from the CH4 uncertainty analysis was applied to the point estimate of non-energy C02 emissions.

1.2. Petroleum Systems Uncertainty

For the petroleum systems analysis, EPA focused on the five highest methane-emitting sources for the year 2016,
which together emitted 78 percent of methane from petroleum systems in 2016, and extrapolated the estimated
uncertainty for the remaining sources. The @RISK add-in provides for the specification of probability density
functions (PDFs) for key variables within a computational structure that mirrors the calculation of the inventory
estimate. The IPCC guidance notes that in using this method, "some uncertainties that are not addressed by
statistical means may exist, including those arising from omissions or double counting, or other conceptual errors,
or from incomplete understanding of the processes that may lead to inaccuracies in estimates developed from
models." The uncertainty bounds reported below only reflect those uncertainties that EPA has been able to
quantify and do not incorporate considerations such as modeling uncertainty, data representativeness,
measurement errors, misreporting or misclassification. The understanding of the uncertainty of emission
estimates for this category evolves and improves as the underlying methodologies and datasets improve.

The results presented below provide the 95 percent confidence bound within which actual emissions from this
source category are likely to fall for the year 2016, using the IPCC methodology. The results of the Approach 2
uncertainty analysis are summarized in Table 2. Petroleum systems CH4 emissions in 2016 were estimated to be
between 27.1 and 51.9 MMT C02 Eq., while C02 emissions were estimated to be between 16.0 and 30.6 MMT C02
Eq. at a 95 percent confidence level. Uncertainty bounds for other years of the time series have not been
calculated, but uncertainty is expected to vary over the time series. For example, years where many emission
sources are calculated with interpolated data would likely have higher uncertainty than years with predominantly
year-specific data.

Table 2. 2018 GHGI Quantitative Uncertainty Estimates for CH4 and Non-energy C02 Emissions from
Petroleum Systems (MMT C02 Eq. and Percent)

Source

Gas

2016 Emissions

Estimate
(MMT CO2 Eq.)

Uncertainty Range Relative to Emissions Estimate3

(MMT CO2 Eq.)

(%)

Lower Boundb

Upper Boundb

Lower Boundb

Upper Boundb

Petroleum Systems

ch4

38.6

27.1

51.9

-30%

+34%

Petroleum Systems0

CO2

22.8

16.0

30.6

-30%

+34%

a - Range of emission estimates estimated by applying the 95 percent confidence intervals obtained from the Monte Carlo Simulation
analysis conducted for the year 2016 CH4 emissions.

b - All reported values are rounded after calculation. As a result, lower and upper bounds may not be duplicable from other rounded values
in the 2018 GHGI report.

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c - An uncertainty analysis for the non-energy C02 emissions was not performed. The relative uncertainty estimated (expressed as a
percent) from the CH4 uncertainty analysis was applied to the point estimate of non-energy C02 emissions.

2. Overview of Uncertainty Analysis in the GHGI (from October 2017
memo)

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).

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 4 below.

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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

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3. Background on Uncertainty for Natural Gas and Petroleum
Systems (from October 2017 memo)

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.

3.1. 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 3 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 3.

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 -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.

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Table 3. Top 12 Emission Sources for Natural Gas Systems in Previous (2011) Uncertainty Analysis for
	GHG Inventory Published in 2011 (2011 GHGI)	



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

3.2. 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 4 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.

Table 4. Top Seven Emission Sources for Petroleum Systems in Previous (2011) Uncertainty Analysis for
	GHG Inventory Published in 2011 (2011 GHGI)	

Source

2011 GHGI CH4
Emissions, year 1995
(MMT CChe)

Shallow Water Offshore Platforms (production segment)

16.1

High-Bleed Pneumatic Controllers (production segment)

9.0

Oil Tanks (production segment)

5.6

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2011 GHGI CH4



Emissions, year 1995

Source

(MMT CChe)

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.

4. Updated Uncertainty Analyses for Natural Gas and Petroleum
Systems in the 2018 GHGI (from October 2017 memo)

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).

4.1. Approach to Estimating Source-specific Uncertainty

For its updated analysis, as in the 2011 GHGI analysis, EPA first identified a select number of "top" emission
sources for each source category.

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Table 5 and Table 6 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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	Table 5. Top 14 Natural Gas Systems CH4 Emission Sources in the 2017 GHGI



Year 2015 Gross Emissions

% of Source

Emission Source (segment)

(MMT C02 Eq.)

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 6. Top 5 Petroleum Systems CH4 Emission Sources in the 2017 GHGI



Year 2015 Gross Emissions

% of Source

Emission Source (segment)

(MMT CO2 Eq.)

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.1 For modeling inputs based on recently published studies (i.e., Marchese et al. and

1 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., natural gas production). Hence, the bootstrap
samples drawn from these GHGRP subpart W data sets were sufficiently large for the purposes of the CLT.

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Zimmerle et al.), uncertainty information available in the study were directly used for the EPA's analyses.2 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.

4.2. Approach to Estimating Aggregate Uncertainty for Total CH4 Emissions Across All
Sources

In response to stakeholder feedback, EPA considered multiple approaches to: 1) estimating uncertainty for non-
top sources; and 2) combining uncertainty for top and non-top sources to determine uncertainty for total CH4
emissions across all sources.

Estimating Uncertainty for Non-top Sources

•	Simple Summation Approach: The uncertainty bounds for total CH4 emissions from non-top sources can
be set equal to the approximated uncertainty bounds for total CH4 emissions from all top sources
combined, which are computed using a simple summation approach. This approach was presented in the
previous version of this memo.

•	Propagation of Error Approach: Similar to the simple summation approach, the uncertainty bounds for
total CH4 emissions from non-top sources can be set equal to the approximated uncertainty bounds for
total CH4 emissions from all top sources combined, which are computed using the propagation of error
formulas (IPCC Approach l3). Propagation of error formulas are most suitable when errors for source-
specific estimates being combined are uncorrelated and random. However, because certain source-
specific parameters are used in estimating emissions from multiple top sources in both natural gas and
petroleum systems, the error terms are expected to be correlated.

•	Monte Carlo Approach: The uncertainty bounds for total CH4 emissions from non-top sources can be
estimated using a multi-step process that relies on Monte Carlo simulation as described below. Due to the
existence of unmodeled sources, this approach is based on IPCC Approach 2, and similar to what EPA used
in the 2011 GHGI petroleum systems uncertainty assessment.

o Step 1 - The uncertainty bounds for total CH4 emissions from all top sources combined is

determined using Monte Carlo simulation,
o Step 2 - The probability density function (PDF) (i.e., normal, lognormal, etc.) along with its
parameters (e.g., mean, standard deviation) of the total CH4 emissions from all top sources
combined is derived by evaluating the obtained distribution in Step 1,
o Step 3 - The PDF for total CH4 emissions from non-top sources can be approximated by the PDF
obtained in Step 2 such that the adjusted mean corresponds to the estimated total CH4 emissions
from non-top sources and the standard deviation corresponds to the product of the ratio of the
estimated total CH4 emissions from non-top sources to-the estimated total CH4 emissions from all
top sources combined.

o Step 4 - The uncertainty bounds around total CH4 emissions from non-top sources can be
estimated using Monte Carlo simulation.

2	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.

3	See discussion beginning in Volume 1, Chapter 3, Section 3.2.3 of 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
http://www.ipcc-neeip.iees.or.ip/public/ep/enelish/6 Uncertaintv.pdf.

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Combining Uncertainty for Top and Non-top Sources to Determine Uncertainty for Total CH4 Emissions Across All
Sources

• Simple Summation Approach: The uncertainty bounds for total CH4 emissions from top and non-top
sources are combined using a simple summation approach. This results in the same 95% uncertainty
bounds for the top, non-top, and overall (i.e., sum of total CH4 emissions from top and non-top sources)
CH4 emissions. Results from this approach were presented in a previous version of this memo.

• Propagation of Error Approach: The uncertainty bounds for total CH4 emissions from top and non-top
sources can be combined by applying propagation of error formulas (IPCC Approach 1). Even though this
approach is easily implementable, it is predicated on the assumption that errors for total CH4 emissions
from top and non-top sources are uncorrelated and random.

• Monte Carlo Approach: The uncertainty bounds for total CH4 emissions from top and non-top sources can
be combined by using Monte Carlo simulations (IPCC Approach 2) where the PDF for total CH4 emissions
from non-top sources is estimated as noted in the Monte Carlo Approach described above.

4.3. Results

Table 7 and Table 8 below summarize calculated source category level uncertainty estimates for natural gas and
petroleum systems, respectively, based on year 2015 emissions from the 2017 GHGI. Note that the reported
uncertainty intervals only reflect those uncertainties that EPA has been able to quantify and do not incorporate
considerations such as modeling uncertainty, data representativeness, measurement errors, misreporting or
misclassification.

According to the IPCC guidelines, if inventory source category uncertainties are correlated, the use of a stochastic
simulation (the Monte Carlo method) is preferable (i.e., IPCC Approach 2) for combining uncertainties provided
that the PDFs and correlation structure of the source-specific uncertainties are quantified. Thus, EPA is
considering using the IPCC Approach 2 for both estimating uncertainty for non-top sources and combining
uncertainty for top and non-top sources for the draft 2018 GHGI.

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Table 7. Summary of Natural Gas Systems Year 2015 CH4 Uncertainty Draft Update Results3

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

44,648,570

-9%

53,729,999

9%

Pneumatic Controllers (Production)

High-bleed Pneumatic Controllers

2,368,036

1,120,764

-53%

3,993,889

69%

Intermittent-bleed Pneumatic Controllers

22,380,215

12,772,278

-43%

33,786,265

51%

Low-bleed Pneumatic Controllers

757,911

122,599

-84%

1,585,524

109%

Station Total Fugitives (Transmission)15

Station, Incl. Compressor Components

2,934,282

1,943,931

-34%

4,265,232

45%

Reciprocating Compressors

8,484,047

5,290,921

-38%

13,237,091

56%

Centrifugal Compressor (Wet Seals)

1,424,742

867,790

-39%

2,301,693

62%

Centrifugal Compressor (Dry Seals)

1,467,867

873,408

-40%

2,370,718

62%

Engine Combustion (Production)

6,323,058

456,914

-93%

22,434,220

255%

Engine Combustion (Transmission)

6,299,036

2,088,945

-67%

8,384,561

33%

Engine Combustion (Processing)

5,806,032

1,959,080

-66%

7,438,841

28%

G&B Episodic Events (Production)

4,879,055

189,869

-96%

26,068,511

434%

Pipeline Venting (Transmission and Storage)

4,590,999

1,210,468

-74%

6,310,292

37%

G&B Pipeline Leaks (Production)

4,038,975

1,537,376

-62%

6,857,736

70%

Station Venting (Transmission)

3,849,139

735,035

-81%

7,436,119

93%

Chemical Injection Pump Venting (Production)

3,034,943

1,999,797

-34%

4,106,240

35%

Liquids Unloading With Plunger Lift (Production)

3,016,831

2,522,890

-16%

3,516,669

17%

Liquids Unloading Without Plunger Lift (Production)

2,211,607

1,689,719

-24%

2,749,717

24%

Shallow Water Offshore Platforms (Production)

3,086,499

453,544

-85%

5,728,239

86%

Separator Fugitives (Production)

2,924,891

1,758,786

-40%

4,162,242

42%

Total for Sources Modeled in Uncertainty Assessment

Simple Summation

139,070,729

84,242,680

-39%

220,463,797

+59%

Propagation of Error Approach

139,070,729

123,166,394

-11%

169,558,504

+22%

Monte Carlo Approach

139,070,729

115,487,232

-17%

167,501,749

+20%

Total for Sources Not Modeled in Uncertainty Assessment

Simple Summation

23,354,601

14,147,148

-39%

37,023,204

+59%

Propagation of Error Approach

23,354,601

20,683,734

-11%

28,474,512

+22%

Monte Carlo Approach

23,354,601

19,141,032

-18%

28,226,024

+21%

Source Category Total

Simple Summation

162,425,331

98,389,827

-39%

257,487,001

+59%

Propagation of Error Approach

162,425,331

146,298,291

-10%

193,340,018

+19%

Monte Carlo Approach

162,425,331

138,228,918

-15%

190,790,170

+17%

a - Referto footnote'd' of Table A1 for explanation of how a key modeling parameter was updated since the previously presented analysis, impacting calculated bounds shown
in this table for transmission station fugitive sources.

Page 12 of 27

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Table 8. 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

638,737

-70%

4,181,207

97%

Intermittent-bleed Pneumatic Controllers

15,887,354

7,755,094

-51%

26,307,172

66%

Low-bleed Pneumatic Controllers

619,806

83,942

-86%

1,442,226

133%

Shallow Water Oil Platforms
(Production)

Shallow Water Oil Platforms

4,207,887

1,053,746

-75%

11,552,844

175%

Associated Gas Flaring &
Venting (Production)

Associated Gas Flaring

2,642,647

1,209,503

-54%

4,459,852

69%

Associated Gas Venting

1,062,962

75,820

-93%

2,626,185

147%

Oil Tanks (Production)

Large Oil Tanks with Flares

202,495

84,691

-58%

340,088

68%

Large Oil Tanks with VRU

99,012

12,738

-87%

209,784

112%

Large Oil Tanks without Controls

1,443,504

593,070

-59%

2,511,470

74%

Small Oil Tanks with Flares

1,726

280

-84%

4,830

180%

Small Oil Tanks without Controls

115,514

4,828

-96%

386,500

235%

Large Oil Tank Separators with
Malfunctioning Dump Valves

149,605

21,233

-86%

533,238

256%

Gas Engine Combustion
(Production)

Gas Engine Combustion

2,254,932

36,467

-98%

7,215,509

220%

Total for Sources Modeled in Uncertainty Assessment

Simple Summation

30,813,532

11,570,150

-62%

61,770,904

+100%

Propagation of Error Approach

30,813,532

21,469,826

-30%

44,926,168

+46%

Monte Carlo Approach

30,813,532

19,504,086

-37%

44,868,886

+46%

Total for Sources Not Modeled in Uncertainty Assessment

Simple Summation

9,062,042

3,402,699

-62%

18,166,386

+100%

Propagation of Error Approach

9,062,042

6,314,124

-30%

13,212,468

+46%

Monte Carlo Approach

9,062,042

5,551,116

-39%

13,716,381

+51%

Source Category Total

Simple Summation

39,875,574

14,972,849

-62%

79,937,290

+100%

Propagation of Error Approach

39,875,574

30,136,175

-24%

54,585,861

+37%

Monte Carlo Approach

39,875,574

27,292,147

-32%

54,063,731

+36%

Page 13 of 27

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5. Requests for Stakeholder Feedback (from October 2017 memo)

The EPA sought feedback on the questions below in the version of this memo released in October 2017. The EPA

continues to welcome additional stakeholder feedback on these questions for potential updates to future GHGIs.

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 5) and petroleum systems (Table 6) for year 2015, and extending the
uncertainty of aggregate emissions estimates for the top emissions sources to the remaining sources (as
illustrated in Table 7 and Table 8).

b.	Using a Monte Carlo Approach to calculate source category total uncertainty (as illustrated in Table 7 and
Table 8).

c.	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

Page 14 of 27

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3. Table 5 and Table 6 and detailed in Appendix A.

4. 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?

5. 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.

6. 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.

7. 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.

8. As shown in tables Al 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%

9. How improved uncertainty results can be used to target improvements for the GHGI.

6. References

Allen, et al. (2013). David T. Allen, Vincent M. Torres, James Thomas, David W. Sullivan, Matthew Harrison, Al Hendler, Scott

C. Herndon, Charles E. Kolb, Matthew P. Fraser, A. Daniel Hill, Brian K. Lamb, Jennifer Miskimins, Robert F. Sawyer, John H.

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 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."

Page 15 of 27

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April 2018

Seinfeld, et al. 2013. "Measurements of methane emissions at natural gas production sites in the United States." Proceedings
of the National. Academy of Sciences, Vol. 110 17768-17773. 2013.

Allen, et al. (2014a). David T. Allen, Adam P. Pacsi, David W. Sullivan, Daniel Zavala-Araiza, Matthew Harrison, Kindal Keen,
Matthew P. Fraser, A. Daniel Hill, Robert F. Sawyer, and John H. Seinfeld. "Methane Emissions from Process Equipment at
Natural Gas Production Sites in the United States: Pneumatic Controllers." Environmental Science & Technology, Vol. 49 633-
640. 2014.

Allen, et al. (2014b). David T. Allen, David W. Sullivan, Daniel Zavala-Araiza, Adam P. Pacsi, Matthew Harrison, Kindal Keen,
Matthew P. Fraser, A. Daniel Hill, Brian K. Lamb, Robert F. Sawyer, and John H. Seinfeld. "Methane Emissions from Process
Equipment at Natural Gas Production Sites in the United States: Liquid Unloadings." Environmental Science & Technology,
Vol. 49 641-648. 2014.

Brandt, et al. (2016). Adam R. Brandt, Garvin A. Heath, and Daniel Cooley. "Methane Leaks from Natural Gas Systems Follow
Extreme Distributions." Environmental Science & Technology, Vol. 5012512-12520. 2016.

IPCC (2006). Montreal: Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme.
Guidelines for National Greenhouse Gas Inventories, Volume 1 General Guidance and Reporting. 2006.

Lamb, et al. (2015). Brian K. Lamb, Steven L. Edburg, Thomas W. Ferrera, Touche Howard, Matthew R. Harrison, Charles E.
Kolb, Amy Townsend-Small, Wesley Dyck, Antonio Possolo, and James R. Whetstone. "Direct Measurements Show Decreasing
Methane Emissions from Natural Gas Local Distribution Systems in the United States." Environmental Science & Technology,
Vol. 49 5161-5169. 2015.

Littlefield, et al. (2017). James A. Littlefield, Joe Marriott, Greg A. Schivley, and Timothy J. Skone. "Synthesis of recent ground-
level methane emission measurements from the U.S. natural gas supply chain." Journal of Cleaner Production, Vol. 148 118-
126. 2017.

Lyon, et al. (2015). David R. Lyon, Daniel Zavala-Araiza, Ramon A. Alvarez, Robert Harriss, Virginia Palacios, Xin Lan, and
Robert Talbot. "Constructing a Spatially Resoved Mehane Emission Inventory for the Barnett Shale Region." Environmental
Science & Technology, Vol. 49 8147-8157. 2015.

Marchese, et al. (2015). Anthony J. Marchese, Timothy L. Vaughn, Daniel J. Zimmerle, David M. Martinez, Laurie L. Williams,
Allen L. Robinson, and Austin L. Mitchell. "Methane Emissions from United States Natural Gas Gathering and Processing."
Environmental Science & Technology, Vol. 49 10718-10727.

Mendenhall, Wackerly, & Scheaffer, 1990. William Mendenhall, Dennis D. Wackerly, and Richard L. Scheaffer. Mathematical
Statistics With Applications. Pws Pub Co (1990).

Zimmerle, et al. (2015). Daniel J. Zimmerle, Laurie L. Williams, Timothy L. Vaughn, Casey Quinn, R. Subramanian, Gerald P.
Duggan, and Bryan Willson. "Methane Emissions from the Natural Gas Transmission and Storage System in the United
States." Environmental Science and Technology, Vol. 49 9374-9383. 2015.

Zavala-Araiza, et al. (2015). Daniel Zavala-Araiza, David R. Lyon, Ramon A. Alvarez, Kenneth J. Davis, Robert Harriss, Scott C.
Herndon, Anna Karion, Eric Adam Kort, Brian K. Lamb, Xin Lan, Anthony J. Marchese, Stephen W. Pacala, Allen L. Robinson,
Paul B. Shepson, Colm Sweeney, Robert Talbot, Amy Townsend-Small, Tara I. Yacovitch, Daniel J. Zimmerle, and Steven P.
Hamburg. "Reconciling Divergent Estimates of Oil and Gas Methane Emissions." Proceedings of the National Academy of
Sciences in the United States of America, Vol. 112 15597-15602. 2015.

Page 16 of 27

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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 (from October

2017	memo)

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 ChU Uncertainty Inputs for @RISK Modeling (from October 2017 memo)

Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

C

Upper Bound
or Maximum

C

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

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

Page 17 of 27

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Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

C

Upper Bound
or Maximum

C

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

-

-

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

-

-

Page 18 of 27

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Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

C

Upper Bound
or Maximum

C

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 datad

Lognormal

3.52

0.5085

-

-

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)

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

-

-

Page 19 of 27

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Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

C

Upper Bound
or Maximum

C

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

-

-

miles

EPA/GRI (1996)

PDF per expert judgment; statistical
parameters imputed using the reported 90%
confidence bound reported

Normal

340,200

20,700

-

-

Page 20 of 27

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April 2018

Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

C

Upper Bound
or Maximum

C

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 datad

Lognormal

3.52

0.5085

-

-

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 - Upon further review, EPA updated the estimated probability density function (PDF) for the station scaling parameter so that the mean of the PDF better corresponded to the
estimated mean CH4 emissions for the source. As a result, the bounds for the sources within this category shifted to the left. The previous station scaling parameter had a lognormal PDF
with a mean of 3.52, standard deviation of 0.51, and shift parameter of 1.59. These were determined by fitting a distribution to the original data provided in the Zimmerle et al. study.
The shift parameter shifts the whole distribution to the right, in this case. The revised station scaling parameter used in this updated memo still has a lognormal PDF with a mean of 3.52
and a standard deviation of 0.51. However, a value of zero was used for the shift parameter to ensure that the mean of the distribution used in the Monte Carlo analysis was equivalent
to the value used in computing the CH4 emissions for the source. This also better aligned with how the other lognormal PDFs were characterized in the remaining source categories (all
with zero-value shift parameters).

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Table A2. Overview of Petroleum Systems Year 2015 Cm Uncertainty Inputs for @RISK Modeling (from October 2017 memo)

Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

C

Upper Bound
or Maximum c

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

-

-

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April 2018

Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

c

Upper Bound
or Maximum c

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

-

-

scf/bbl (large with VRU)

Subpart WRY2015

Statistical analysis of reported data

Normal

0.47

0.21

-

-

throughput fraction (large with
VRU)

Subpart W RY2015

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

-

-

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April 2018

Emissions Calculation Input

Input Basis a

Uncertainty Basis

PDF

Mean or Point
Estimate or Most
Likely Value

Standard
Deviation b

Lower Bound
or Minimum

C

Upper Bound
or Maximum c

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%
confidence bound calculated for 1993
estimate

Lognormal

6.30

4.95

-

-

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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April 2018

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 often differ significantly from the studies underlying GHGI
methodologies. For example, the GRI/EPA study generally had smaller sample sizes and more rudimentary techniques for
developing nationally-applicable emissions and activity factors from the collected data than the more recent bottom up
studies that were used for 2015 estimates in the 2017 GHGI.

Table Bl. Comparison of Quantitative Uncertainty Estimates for CH4 Emissions from Natural Gas Systems (MMT
CO2 Eq. and Percent)

Segment

Study

Year

Emissions
(MMT CO2
Eq.)

Uncertainty Rangea

MMT CO2 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%

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

21.32

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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Table B2. Comparison of Quantitative Uncertainty Estimates for ChU Emissions from Petroleum Systems (MMT CO2

Eq.and Percent)

Segment

Study

Year

Emissions
(MMT CO2
Eq.)

Uncertainty Rangea

MMT CO2 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%

Oil and Gas, All Segments,
Barnett Shale

Zavala, et al. (2015)c

2013

12.9

10.5

16.0

-19%

24%

Table B3. Comparison of Quantitative Uncertainty Estimates for CH4 Emissions from Specific Emission Sources from

Natural Gas Systems (MMT CO2 Eq. and Percent)

Segment & Emission
Source

Study

Year

Emissions
(MMT CO2
Eq.)

Uncertainty Rangea

MMT CO2 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

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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