FOREWORD 

The U.S. Environmental Protection Agency is charged by Congress with protecting the Nation1

environmental 

S land. air, and water resources. Under a mandate of national 

laws. the Agency strives to formulate and implement actions leading to a compatible balance between humar. activities and the ability of natural 

program 

systems to support and nurture life. To meet this mandate, EPA's research 

blems 

is providing data and technical support for solving environmental protoday and building a science knowledge base necessary 

logical 

to manage our ecovent 

resources wisely, understand how pollutants affect our health, and preor reduce environmental risks in the future. 

The National Risk Management Research Laboratory is the Agency• s center for 

investigation of technological and management approaches for reducing risks 

from 

research 

threats to human health and the environment. The focus of the Laboratory• s 

land~ water. 

program is on methods for the prevention and control of pollution to air. 

systems; 

and subsurface resources;: protection of water quality in public water 

control of 

remediation of contaminated sites and groundwater: and prevention and 

indoor air pollution. The goal of this research effort is to catalyze 

development and implementation of innovative. cost-effective enVironmental 

technologies; develop scientific and engineering 

support 

information needed by EPA to 

mation 

regulatory and policy decisions; and provide technical support and infOrtransfer to ensure effective implementation of environmental regulations 

and strategies. 

'This publication has been produced as part of the Laboratory• s strategic longterm research plan. It is published and made av~able by EPA1

search 

s Office of Reand Development 

with 

to assist the user community and to link researchers 

their clients. 

E. Timothy Oppelt. Director 

National Risk Management Research Laboratory 

EPA REVIEW NOTICE 

This report has been peer and administratively reviewed by the U.S. Environmental 

Protection Agency, and approved for publication. Mention of trade names or 

commercial products does not constitute endorsement or recommendation for use. 

This document is available to the public through the National T echnicallnformation 

Service, Springfield, Virginia 22161. 
METHANE EMISSIONS FROM 

THE NATURAL GAS INDUSTRY, 

VOLUME 5: ACTIVITY FACTORS 

FINAL REPORT 

Prepared by: 

Blake E. Stapper 

Radian International LLC 

8501 N. Mopac Blvd. 

P.O. Box 201088 

Austin, TX 78720-1088 

DCN: 95-263-081-14 

For 

GRI Project Manager: Robert A. Lott 

GAS RESEARCH INSTITUTE 

Contract No. 5091-251-2171 

8600 West Bryn Mawr Ave. 

Chicago, IL 60631 

and 

EPA-600 /R-S6-080e 

June 1996 

EPA Project Manager: David A. Kirchgessner 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

Contract No. 68-01-0031 

National Risk Management Research Laboratory 

Research Triangle Park, NC 27711 
DISCLAIMER 

LEGAL NOTICE: This report was prepared by Radian International LLC as an account 

of work sponsored by Gas Research Institute (GRI) and the U.S. Environmental Protection 

Agency {EPA). Neither EPA, GRI, members of GRI, nor any person acting on behalf of 

either: 

a. 

b. 

Makes any warranty or representation, express or implied, with respect to the 

accuracy, completeness, or usefulness of the information contained in this 

report, or that the use of any apparatus, method, or process disclosed in this 

report may not infringe privately O'Mled rights; or 

Assumes any liability with respect to the use of, or for damages resulting 

from the use of, any information, apparatus, method, or process disclosed in 

this report. 

NOTE: EPA's Office of Research and Development quality assurance/quality control 

(QNQC) requirements are applicable to some of the count data generated by this project. 

Emission data and additional count data are from industry or literature sources, and are not 

subject to EPA/ORD's QA/QC policies. In all cases, data and results were reviewed by the 

panel of experts listed in Appendix D of Volume 2. 

ii 
Title 

Contractor 

Principal 

Investigator 

Report Period 

Objective 

Technical 

Perspective 

Results 

RESEARCH SUMMARY 

Methane Emissions from the Natural Gas Industry, 

Volume 5: Activity Factors 

Final Report 

Radian International LLC 

GRJ Contract Number 509!-251-2171 

EPA Contract Number 68-Dl-0031 

Blake E. Stapper 

March 1991 - June 1996 

Final Report 

This report describes a study to quantify the national activity factors for 

the methane emission source types in the gas industry. 

The increased use of natural gas has been suggested as a strategy for 

reducing the potential for global warming. During .combustion, natural 

gas generates less carbon dioxide (C02) per unit of energy produced than 

either coal or oil. On the basis of the amoturt of C02 emitted, the 

potential for global wanning could be reduced by substituting natural gas 

for coal or oil. However, since natural gas is primarily methane, a potent 

greenhouse gas, losses of natural gas during production, processing, 

transmission, and distribution could reduce the inherent advantage of its 

lower C02 emissions. 

To investigate this, Gas Research Institute (GRJ) and the U.S. 

Environmental Protection Agency's Office of Research and Development 

(EPNORD) cofunded a major study to quantify methane emissions from 

U.S. natural gas operations for the 1992 base year. The results of this 

study can be used to construct global ID:ethane budgets and to determine 

the relative impact on global wanning of natural gas versus coal and oil. 

Activity factors are documented for use in determining the total 

emissions from the natural gas industry. Since there are very few 

published activity factors for the gas industry, many activity factors were 

developed specifically for this study. Confidence intervals were 

calculated for the activity factors so that the overall accuracy could be 

determined. Precautions were also taken to ensure that the activity 

factors are statistically representative of the industry. 

iii 
Technical 

Approach 

Project 

Implications 

The program reached its accuracy goal and provides an accurate estimate 

of methane emissions that can be used to construct U.S. methane 

inventories and analyze fuel switching strategies. 

Since it is impractical to sample emissions from every source in the 

natural gas industry, it is necessary to develop a method of scaling up the 

sampled emissions from a representative set of sources within the 

industry. The activity factor extrapolation method was developed for this 

purpose. 

This method is used to scale-up the average annual emissions from a 

source (determined by a limited sampling effort) to represent the entire 

emissions from the national population of similar sources in the gas 

industry. The typical activity factor extrapolation approach uses 

emission factors (EF) and activity factors (AF) to do this. An emission 

factor for a source category is defined as the average annual emissions 

per source. Activity factors are the number of sources in the entire target 

population or source category. Thus, the product of the EF and the AF 

equals the annual national emissions from a specific source in the natural 

gas industry. Since there are very few published activity factors for the 

gas industry, many were developed specifically for this study. 

The accuracy of the activity factors is dependent upon precision and bias. 

The precision of the activity factors was determined by calculating the 

90% confidence limits. Potential bias in the activity factors were 

eliminated by three methods: peer review by experienced sources, 

analysis of the data, and extrapolation by different parameters. 

For the 1992 base year the annual methane emissions for the U.S. natural 

gas industry are 314 Bscf ± 106 Bscf (~ 34%). This is equivalent to 

1.4% ± 0.5% of 1992 gross natural gas production. Results from this 

program were used to compare greenhouse gas emissions from the fuel 

cycle for natural gas, oil, and coal using the global warming potentials 

(GWPs) recently published by the Intergovernmental Panel on Climate 

Change (IPCC). The analysis showed that natural gas contributes less to 

potential global warming than coal or oil, which supports the fuel 

switching strategy suggested by IPCC and others. 

In addition, results from this study are being used by the natural gas 

industry to reduce operating costs while reducing emissions. Some 

companies are also participating in the Natural Gas-Star program, a 

voluntary program sponsored by EPA's Office of Air and Radiation in 

cooperation with the American Gas Association to implement costeffective emission reductions and to report reductions to EPA. Since this 

program was begun after the 1992 baseline year, any reductions in 

IV 
methane emissions from this program are not reflected in this study's 

total emissions. 

Robert A. Lott 

Senior Project Manager, Environment and Safety 

v 
TABLE OF CONTENTS 

Page 

1.0 SUMMARY ................................................. I 

2.0 INTRODUCTION 2 

3.0 GAS INDUSTRY CHARACTERIZATION . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

3.1 Natural Gas Industry Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

3.2 Production Segment Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 

3.3 Gas Processing Segment Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

3.4 Transmission and Storage Segment Definition . . . . . . . . . . . . . . . . . . . . 10 

3.5 Distribution Segment Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 

4.0 DEVELOPMENT OF ACTIVITY FACTORS ......................... IS 

4.1 Definition of Activity and Emission Factors . . . . . . . . . . . . . . . . . . . . . . 15 

4.2 Effect of Sampling Approach on Activity Factors . . . . . . . . . . . . . . . . . . 15 

4.3 Activity Factor Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 6 

4.4 Extrapolation of Site Activity Factor Data . . . . . . . . . . . . . . . . . . . . . . . 17 

4.4.1 Selection of Site-Weighted (Ratio) Extrapolation Technique ..... 19 

4.4.2 Extrapolation Parameter Selection ....................... 20 

4.5 Activity Factor Accuracy ................................... 21 

4.5.1 Precision ......................................... 23 

4.5.2 Propagation of Error ................................. 24 

4.5.3 Screening for Bias .................................. 26 

5.0 PRODUCTION SEGMENT ...................................... 28 

5.1 Development of a Regional Approach ............... , .......... 28 

5.2 Gas Well Count and Production (Onshore and Offshore) . . . . . . . . . . . . . 34 

5.3 Oil Well Count and Production (Onshore and Offshore) ............. 34 

5.4 Oil Wells Marketing Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 

5.5 Offshore Platforms ....................................... 36 

5.6 Specific Equipment ......... , ... , . . . . . . . . . . . . . . . . . . . . . . . . . 39 

5.7 Vessels and Pressure Relief Valves ............................ 40 

5.8 Gathering Pipeline Miles ................................... 46 

5.9 Maintenance Activities ..................................... 48 

5.1 0 Compressors and Dehydrators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 

vi 
TABLE OF CONTENTS 

(Continued) 

Page 

6.0 GAS PROCESSING SEGMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 

6.1 Gas Plaut Count . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 I 

6.2 Acid Gas Recovery (AGR) Units ............................. 52 

6.3 Compressors and Dehydrators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 

7.0 TRANSMISSION SEGMENT .................................... 53 

7.1 Compressor Station Count .................................. 53 

7.2 Storage Station Count ..................................... 56 

7.3 Storage Well Count ....................................... 57 

7.4 M&R Station Count ....................................... 57 

7.5 Miles of Pipeline ......................................... 60 

7.6 Compressors and Dehydrators ................................ 60 

7. 7 Pneumatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 

8.0 CROSS-SEGMENT ACTIVITY FACTORS .......................... 61 

8.1 , Compressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 

8.1.1 Compressor Horsepower-Hours ......................... 61 

8.1.2 Compressor Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 

8.2 Dehydrators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 

8.2.1 Dehydrator Count . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 

8.2.2 Dehydrator Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 

8.2.3 Dehydrator Flash Drums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 

8.2.4 Dehydrator Gas Pumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 

8.2.5 Dehydrator Stripping Gas Use . . . . . . . . . . . . . . . . . . . . . . . . . . 75 

8.2.6 Dehydrator Vapor Recovery Use ........................ 76 

9.0 DISTRIBUTION SEGMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 

9.1 Distribution Annual Equivalent Leaks . . . . . . . . . . . . . . . . . . . . . . . . . . 78 

9.2 Distribution Metering and Pressure Regulating Stations . . . . . . . . . . . . . . 85 

9.3 Customer Meters ......................................... 87 

9.4 Distribution Mains and Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 

!0.0 RESULTS .................................................. 90 

11.0 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 

APPENDIX A - Site Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-I 

APPENDIX B - Methods for Estimating Activity Factors . . . . . . . . . . . . . . . . B-1 

APPENDIX C - Conversion Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1 

vii 
LIST OF FIGURES 

Page 

3-1 Gas Industry Flow Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

3-2 Industry Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... 7 

3-3 Gas Processing Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II 

3-4 Transmission and Storage Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

3-5 Distribution Segment Equipment .................................. 14 

5-l Major Producing States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

5-2 Selected Production Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 

B-1 Conceptual Comparison of Nonnal and Lognormal Distributions . . . . . . . . . . . B-4 

B-2 Comparison of 90% Confidence Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-7 

B-3 Distribution of Separators per Well .................... · . . . . . . . . . . . . B-8 

B-4 Ratio of Separators per Unit of Production . . . . . . . . . . . . . . . . . . . . . . . . . . B-9 

viii 
LIST OF TABLES 

Page 

4-l Nationally-Tracked Activity Factors ................................ 17 

4-2 Production Extrapolation Parameter Selection . . . . . . . . . . . . . . . . . . . . . . . . . 22 

4-3 Error Propagation for a Swn ..................................... 25 

4-4 Error Propagation for a Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

5-l Regional Differences in Production Rates and Well Counts ................ 31 

5-2 Sample Set Comparison to Actual Regional Data . . . . . . . . . . . . . . . . . . . . . . . 33 

5-3 National Gas Industry Wells ..................................... 37 

5-4 Platfonn Counts .............................................. 39 

5-5 Specific Equipment Activity Factors ................................ 40 

5-6 Separators in Production . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 41 

5-7 In-Line Heaters in Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

5-8 Pneumatic Devices in Production ...................... , . . . . . . . . . . . 43 

5-9 Chemical Injection Pumps in Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

5-10 Compressors (Non-Gas Lift) in Production . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

5-11 Pressure Relief Valves ......................................... 46 

5-12 Production Gathering Pipeline Mileage - Gas Wells . . . . . . . . . . . . . . . . . . . . . 48 

6-1 Divisions of Gas Processing Plants by Type .......................... 51 

7-1 Transmission Compressor Station Counts ............................ 54 

7-2 Transmission M&R Station Populations ............................. 59 

8-1 Compressor Activity Factors for Each Industry Segment .................. 62 

8-2 Compressor Counts in the Gas Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 

ix 
LIST OF TABLES 

(Continued) 

Page 

8-3 U.S. Gas Industry Dehydrator Counts ............................... 71 

8-4 Industry Segment Glycol Dehydrator Throughput . . . . . . . . . . . . . . . . . . . . . . . 73 

8-5 U.S. Gas Industry Dehydrator Flash Drums ........................... 76 

8-6 Site Survey Gas Pump Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 

8-7 Site Survey Stripping Gas and Vapor Recovery Data .................... 77 

9-1 National Leak Repairs Allocated by Pipe Material Category . . . . . . . . . . . . . . . 80 

9-2 Summary of Leak Record Data from Participating Companies .............. 83 

9-3 Summary of Activity Factors for Distribution Underground 

Pipelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 

9-4 Activity Data Provided by Individual Companies . . . . . . . . . . . . . . . . . . . . . . . 86 

9-5 Summary of Activity Factors for Distribution Metering/ 

Pressure Regulating Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 

!0-I Well-Defined Activity Factors .................................... 90 

I0-2 Examples of Developed Activity Factors ........ _ ..................... 91 

A-I Production Sites (Summary) .................................... A-2 

A-2 Production Sites (Offshore Data) ................................. A-3 

A-3 Production Sites (Gulf Coast Onshore Data) ......................... A-4 

A-4 Production Sites (Central Plains Data) .............................. A-5 

A-5 Production Sites (Pacific/Mountain Onshore Data) ..................... A-6 

A-6 Production Sites (Atlantic & Great Lakes Data) ....................... A-7 

A-7 Gas Processing Plants ......................................... A-9 

X 
LIST OF TABLES 

(Continued) 

Page 

A-8 Transmission Compressor Stations ............................... A-10 

A-9 Storage Compressor Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-12 

XI 
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1.0 SUMMARY 

This document is one of several volumes that provide background 

information supporting the Gas Research Institute and U.S. Environmental Protection 

Agency Office of Research and Development (GRJ-EP NORD) methane emissions project. 

The objective of this comprehensive program is to quantify the methane emissions from the 

gas industry for the 1992 base year to within± 0.5% of natural gas production starting at 

the wellhead and ending immediately downstream of the customer's meter. Activity factors 

are needed to estimate the national emissions from the natural gas industry and are based on 

estimated populations of equipment and equipment characteristics. This report presents a 

definition for activity factors, methods of calculation, and this project's calculated values 

for industry activity factors. 

I 
2.0 INTRODUCTION 

If emissions could be sampled from every source in the natural gas industry, 

then the total national emissions for the industry could be determined by summing the 

emissions from each source. Unfortunately, because of the size of the industry, measuring 

emissions from each source is impractical. Therefore, a method of scaling up (i.e., 

extrapolating) the sampled emissions from a representative set of sources within the 

industry is necessary. The activity factor extrapolation method was developed for this 

purpose. 

This method is used to scale-up the average annual emissions from a source 

(determined by a limited sampling effort) to represent the entire emissions from the national 

population of similar sources in the gas industry. The typical activity factor extrapolation 

approach uses emission factors (EF) and activity factors (AF) to do this. An emission 

factor for a source category is defined as the average annual emission per source. Activity 

factors are the number of sources in the entire target population or soiJ.rce category. An 

activity factor is usually presented as an equipment count, but a few exceptions exist, such 

as: I) horsepower ·hours for compressors, 2) average equivalent leaks for underground 

pipe, and 3) events/year for maintenance activities. They can be used to estimate the 

nationwide emissions from the natural gas industry since emission rates are often measured 

on a per equipment or per activity basis. Thus, the emission factor and activity factor are 

defined such that their product equals the annual national emissions from a specific source 

in the natural gas industry. 

EF x AF = National Emission Rate (I) 

Since there are very few published activity factors for the gas industry, many 

were developed specifically for this study, especially in the production segment of the 

industry. This report discusses precautions taken to ensure that the data used to detennine 

activity factors were statistically representative of the industry. 

2 
This report explains in detail how the activity factors for the GRJ/EPA 

methane emissions project were determined for the 1992 base year. The following sections 

summarize the methods used to calculate gas industry activity factors. Section 3 provides 

the general industry characterization used to identify the equipment populations. Section 4 

defines the techniques used to develop the activity factors. Sections 5 through 9 provide 

the specific activity factor development for equipment within each segment of the gas 

industry. Summary results are presented in Section 10. This report is one of several 

volumes under the GRI/EPA methane emissions project. 

3 
3.0 GAS INDUSTRY CHARACTERIZATION 

Most activity factors define equipment populations. Therefore, to determine 

activity factors, it is important to first define and describe the major divisions in the 

industry that affect the selection of specific equipment. This section of the report 

characterizes the industry by outlining the segments of the industry as well as the types of 

equipment found within each segment. 

\Vhile this section draws a general picture of the industry developed by the 

GRIIEPA methane emissions project, it is not intended to represent a defmitive picture of 

the industry regarding all typical operational parameters. Many details of the operation are 

wmecessary for development of an accurate methane emissions inventory. Other details are 

given in specific reports where the details were needed to estimate specific emission factors. 

3.1 Natural Gas Industry Definition 

The natural gas industry produces and delivers natural gas to various 

residential, commercial, and industrial customers. The industry uses wells to produce 

natural gas existing in underground fonnations, then processes and compresses the gas, and 

transports it to the customer. Transportation to the customer involves intra and interstate 

pipeline transportation, storage, and finally distribution of the gas by local distribution 

pipeline networks to the customer. 

The generally accepted segments of the natural gas industry are 1) 

production, 2) gas processing, 3) transportation, 4) storage, and 5) distribution. Each of 

these segments is shown in the overall flow chart for the industry in Figure 3-l. Each 

segment is described in more detail in the following subsections. 

This project has set specific boundaries on each segment of the industry that 

specify what equipment is included or excluded from the study. These boundaries were set 

4 
I I 

I I 

PRODUCTION I PROCESSING I TRANSMISSION/ I I 

I I STORAGE 

I I Direct 

I I Sales 

I I Compressor 

Stations 

c 

Pipelines 

Surface Facilities Gas Plant 

i I 

c 

Liquids 

Storage 

Liquids 

Underground 

Storage 

Reservoir 

Figure 3-1. Gas Industry Flow Cbart 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

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I 

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I 

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I 

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DISTRIBUTION 

M&PR Stations 

Main and 

Service Pipelines 

Customer Meters 

0 Compressor 

@ Meter 

~ Pressure l*f Regulator 
using input from industry experts. The guideline used for setting the boundaries was to 

exclude equipment in each segment not required for the marketing of natural gas. For 

example, certain oil production equipment is excluded from the production segment since it 

exists to produce oil and is not needed for gas production (see Figure 3-2). Similarly, in 

gas processing, equipment associateC with the fractionation of propane, butane, and natural 

gas liquids is excluded. In distribution, all equipment up to and including the customer's 

meter are included. End user piping, combustion, and vented emissions are not included. 

3.2 Production Segment Definition 

Emissions of methane that result from oil production, or that occur naturally 

(non-anthropogenic) from formations are excluded. Unmarketed natural gas, such as that 

produced by oil wells that vent some gas, are not considered part of the gas industry. 

The production segment is composed of gas wells, oil wells, and surface 

equipment. The well includes the holes drilled through subsurface rock that reach the 

producing formation, and the subsurface equipment such as casing and tubing pipe. Gas 

and oil surface equipment can include separators, heaters, heater-treaters, tanks, dehydrators, 

compressors, pumps, and pipelines. 

However, the segment definition for gas industry production equipment 

excludes certain equipment mainly associated with oil production. Figure 3-2 shows the 

general equipment found in the oil and gas production segment as well as the selected 

boundaries for gas industry equipment used by this study. Equipment outside the 

boundaries were not included in this study's activity factor estimates. 

As shown in Figure 3·2, the gas industry production segment includes all 

equipment at gas well sites, except emissions from liquid hydrocarbon condensate or oil 

tanks at the site. These emissions are considered to be a res_ult of marketing the liquid 

products from the welL The gas industry production segment excludes equipment that 

6 
-"' 

-

"" :::: 

·--

0 

o,,w~:, r--fi EJ WeiJhe~ ~ 

Compressor 

Oo!Well 

Wellhead,---------

(producJng 

gas) 

Coal Bed 

Methalle We!! 

Gas Well 

Wellhead 

CbcrrucaJ 

!n;e<UOn 

Pump r>--

Petroleum industry 

Compressor 

f---~·,;h~~o. ~ Foeld Use 

Gu 

.-------~ 

- ~ Vapor Recovery 

Compres.sor 

Petroleum Industry 

Figure 3-2. Industry Boundaries 

7 
exists primarily for the production of oil, since it would exist even if the gas were not 

marketed. Therefore, the definition excludes all oil tanks, and excludes equipment at ali oil 

wells that do not market gas. In addition, it excludes much of the equipment at oil wells 

that do market gas. At oil wells that market gas production, the gas production is 

secondary and usually generates lower revenue; the well exists primarily because it 

produces oil. Therefore the wellhead, the separator, the pneumatic control valves, the 

well's chemical injection pumps, any field use gas lines, and all of the liquid piping are 

considered part of the oil industry and are excluded from the GRI/EPA gas industry study. 

The gas industry equipment begins only on the gas line do\oVllstream of the separator, at the 

first piece of gas line equipment, such as the sales meter, compressor, or dehydrator. 

In general, an oil or oil and gas field may have centralized surface treatment 

facilities, or each well site may have its own independent surface facilities. In centralized 

facilities, all of the separators, dehydrators, and compressors may be in one location, with 

gas flowing in from gathering pipelines connected to many wellheads. Decentralized 

' 

facilities have all the necessary surface equipment (separators, compressor, dehydrator, etc.) 

at each individual well site. Centralized facilities can have lower equipment counts per 

well than decentralized facilities. Sometimes the facilities may be primarily decentralized, 

but have a few centralized components. For example, separators may be at each well 

(decentralized), while compression and dehydration may be centralized. 

Whatever the field configuration, all gas wells have a wellhead and most 

have a gas meter. Also independent of the field configuration, gas wells may or may not 

have separator(s), a dehydrator, or a compressor. The use of the equipment depends upon 

the free liquid production, the absorbed moisture content, and the well pressure. For 

example, some sweet, dry gas wells can produce directly to a pipeline. However, most 

wells require separation for free-liquiC products (salt water, hydrocarbon condensate, and 

oil), and some dehydration. 

8 
Oil wells that market gas (the only oil wells included in this study) may also 

have centralized or individual well site facilities. They will always have a separator and a 

meter. As with gas wells, they may or may not have a dehydrator or a compressor, 

depending upon the absorbed moisture content and the pressure. 

Oil wells that market gas may be either free-flowing or artificial-lift wells. 

Free-flowing wells often also have absorbed or co-produced gas that is marketed. 

Therefore, some of the equipment at these free-flowing oil wells is considered part of the 

gas industry if it exists to market the gas. Artificial-lift oil wells are most often not part of 

the gas industry, but a few do produce gas, and those few therefore are included in the gas 

industry definition. 

Artificial-lift oil wells that have downhole pwnps or surface pwnp jacks 

usually do not produce or market any gas and therefore are not part of the gas industry. 

Artificial-gas lift oil wells push compressed gas downhole and inject the gas into the tubing, 

thus using the gas to aerate the oil in the tubing string. This brings the oil back to the 

surface. Only the gas-lift wells that produce and market gas in excess of the amount 

injected are considered part of the natural gas industry. For gas-lift oil wells that market 

gas, the compressors associated with the gas-lift circulation are not considered to be part of 

the gas industry. 

3.3 Gas Processin~ Segment Definition 

Natural gas processing plants exist primarily to recover high value liquid 

products from the gas stream and to maintain the quality (content and heating value) of the 

gas stream. The liquid products include natural gasoline, butane, and propane (ethane is 

sometimes recovered as well). The products are removed by compression and cooling or 

by absorption. Absorption processes use a fluid, such as lean oil, to absorb the liquid 

components from the gas stream in a tower~ the rich oil is then heated to release the 

9 
recovered products. A compression and cooling process uses a turboexpander or a 

refrigeration process to supercool the natural gas so that the products will condense. 

A gas plant may have fractionation towers and stabilization towers to further 

purify the individual components of the product stream. The back end of the gas plant, 

such as the fractionation train, is excluded from the gas industry definition since it exists to 

purify and market liquid products. The back end of the gas plant has negligible methane 

emissions since the liquids handled contain only trace amounts of methane. 

The front end of the gas plant often contains dehydration facilities, wet gas 

compression, and the absorption or compression and cooling process. All gas plants are 

considered part of the natural gas industry. Therefore, all methane emissions from natural 

gas processing plants are included in this study. Figure 3·3 shows a schematic diagram of 

a gas plant. 

3.4 Transmission and Stora~e See:ment Definition 

The transmission segment moves the natural gas from the gas plant or 

directly from the field production to the local distribution companies. Gas is often moved 

across many states, such as from the Gulf Coast to the Eastern seaboard of the United 

States. The segment consists of large diameter pipeline, compressor stations, and metering 

facilities. All of these facilities and all of the equipment they contain are considered part of 

the natural gas industry. 

Transmission compressor stations usually consist of piping manifolds, 

reciprocating or gas turbine (centrifugal) compressors, and generators. Dehydrators may be 

included but are not usua1Iy present because of upstream drying. The station may also 

include metering facilities. Figure 3-4 shows a schematic diagram of transmission and 

storage stations. 

10 
c 

Dehydration 

Absorption or 

Compression 

& Cooling Process 

Liquids 

Backend of the Plant 

,--------------

1 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

C' 

' 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

Natural I 

Gasollnes I 

---------------

Figure 3-3. Gas Processing Plant 

!I 
-N 

Gas 

from 

Pipeline 

- ' 

TRANSMISSION ABOVE GROUND STORAGE 

COMPRESSOR STATION 

fD Cooling 

~ Compressors LNG Process Tank 

c 

Heaters ~ 1 

c 

Gas Transmission Pipeline 

c from 

Pipeline 

BELOW GROUND STORAGE 

c 

Compressor 

c 

$~~ Station 

Wells 

Dehydrator ~ ~ ~ Storage Field 

Figure 3~4. Transmission and Storage Stations 
Transmission companies also have metering and regulating stations where 

they exchange gas with other transmission companies, or where they deliver gas to the local 

distribution companies (LDCs). Storage facilities exist to store natural gas produced during 

off-peak times (summer) so that the gas can be produced and delivered during periods of 

peak demand in the winter. Storage facilities are often located close to consumption centers 

so that the cross-country transmission pipeline dOes not have to be sized for peak winter 

demand. Storage facilities can be below ground or above ground. Above-ground facilities 

are liquified natural gas (LNG) facilities that liquify the gas by supercooling and then store 

the liquid-phase methane in above ground, heavily insulated storage tanks. Below-ground 

facilities compress and store the gas (in vapor phase) in one of several fonnations: 1) spent 

gas production fields, 2) aquifers, or 3) salt caverns. Below-ground storage is the 

predominant means of gas storage. 

Most storage stations con~ist of a compressor station that is very similar to a 

transmission compression station (see Figure 3-4). Underground storage facilities also have 

storage field wells, and often have dehydrators to remove the water absorbed by the gas 

while underground. Except for emissions from underground leaks in storage formations, all 

storage equipment is included in the boundaries for the gas industry defined by this project. 

3.5 Distribution se,ment Definition 

The distribution segment receives high-pressure gas from the transmission 

pipelines, reduces the pressure, and delivers the gas to all of the residential, commercial, 

and industrial consumers. The segment includes pipeline (mains and services), meter and 

regulating stations (city gates), and customer meters. All of these facilities are considered 

to be an integral part of the gas industry. 

Figure 3-5 shows a schematic of the distribution segment and associated equipment. 

13 
Meter and Pressure 

Regulating Pressure-Reducing (M&PR) Station 

Station 

Mains (Pipeline) 

~ 

t?. 

--.. 

M 

Transmission t?. 

Pipeline Meter 

Pressure-Reducing 

Regulators 

Figure 3-5. Distribution Segment Equipment 

Services (Small Pipe) M 

M 

M 

M 

M 

M 

M 

M 

M 

Customer Meters 

(Residential, 

Commercial, 

Industrial, 

Electric Uli!ity) 
4.0 DEVELOPMENT OF ACTIVITY FACTORS 

This section discusses the sampling approach, sources of equipment counts, 

and methods used to develop activity factors. 

4.1 Definition of Activity and Emission Factors 

The activity factor is typically defined as a count of the total population of 

sources in a particular category, and the emission factor is defined as the average annual 

emissions per source. Where a source category is emissions from an equipment type, the 

activity factor is the national population of that equipment type. Exceptions to the general 

definitions for the factors are those sources that have an emission rate that can be more 

accurately represented by a parameter that directly influences the rate of operation. (For 

example, the emission factor for internal combustion (I C) engines is given in terms of 

annual emissions per horsepower.) For such exceptions, the activity factor is defined as the 

parameter that influences the emissions from the source (e.g., the activity factor is annual 

horsepower-hours from the IC engine). 

4.2 Effect of Sampling Approach on Activity Factors 

As discussed in Volume 3 on general methodology, 1 the methods used to 

measure and collect samples directly impact the generation of activity faCtors. Improper 

sampling can easily bias the results. For this reason, particular attention was paid to the 

sampling methods used for this project. However, an absolutely random sample of the 

natural gas industry could not be ensured because participation in this program was 

voltmtary. 

In addition to ensuring as random of a sample as possible, the project team 

attempted to identify and compensate for any biases in the data from the sampling. For 

example, if regional differences in equipment configurations were kno\VIl to exist, then data 

15 
were gathered and evaluated regionally to generate regional activity factors. These regional 

activity factors were then combined to detennine a national total activity factor for a 

particular source type. 

The project team attempted to compensate for each of the biases identified by 

adjusting the sampling method used. For the example of regional bias, the United States 

was divided into six regions to account for the regional differences in most of the activity 

factors, and samples were taken in each of those regions (see Section 5 for more details). 

No significant regional differences were observed in the major activity factors in the gas 

processing, transmission, or distribution segments; in these categories all samples were 

treated equally and averaged to produce a national figure. 

4.3 Activity Factor Sources 

Activity factors were determined from publications such as the American Gas 

Association (A.G.A.) Gas Facts/ company data, or through site visits. 

Organizations such as A.G.A. track national statistics such as gas wells, miles 

of transmission and distribution pipelines, and total n~tional production in the naturil gas 

industry. Table 4-1 shows some of the published and well-defined activity factors. 

However, in many cases, the total population of a source type within the gas industry is 

unknown. 

For sources that are not tracked nationally, data from individual companies 

(for the entire company) or regional surveys (surveys by state agencies or trade 

organizations) are sometimes available. Metering/pressure regulating stations, glycol 

dehydrators, and compressor engines/gas turbines are trac.:ked on a company-wide basis or 

by regional organizations. For regional- or company-tracked activity factors, sufficient 

company/regional data had to be gathered to comprise a representative sample to 

extrapolate to a national population. 

16 
TABLE 4-1 NATIONALLY-TRACKED ACTIVITY FACTORS . 

Segment Activity Factor Name Number 

T a tal Industry 1992 Gross Gas Production (Tscf) 22.13 

1992 . 

Marketed Gas Production (Tscf) 18.71 

Production No. of Gas Wells 276,000 

No. of Oil Wells 602,200 

Processing No. of Gas Plants 726 

Transmission and Storage Miles of Trans Pipeline 284,500 

No. of Storage Facilities 475 

No. of Storage Wells 18,000 

Distribution Miles of Mains 836,760 

No. of Services 51,520,000 

For sources that have an unknown population, a number of site visits were 

conducted to determine the number of sources at each location. The average values Irom 

multiple sites were then scaled-up to represent the total population. Site visits to collect 

activity factor data were typically done in conjunction with the data collection efforts for the 

emission factors. For the activity factors determined from site visit data, a method was 

needed for extrapolating the site count to a national value. The following section explains 

how the activity factors were determined from site visit data. 

4.4 Extrapolation of Site Activity Factor Data 

In the GRJ/EPA study, some activity factors were determined based on 

extrapolative site data. For example, no data were available on the nationwide population of 

17 
production separators. The number of separators at a site, gathered as part of site visits, was 

divided by the number of wells at each site. Then, the average ratio of separators to wells 

from all site visit data was used to extrapolate nationally by multiplying by the national well 

count. This method of scaling-up the site visit data uses extrapolation parameters (EP), a 

well-defined, published factor that can relate the activity between the site and the national or 

regional level. The EPs are usually one of the nationally-tracked activity factors presented in 

Table 4-1. 

Equipment activity factors from one individual site's data (AF.J can be 

extrapolated to regional or national activity factors (AFR or AFN, respectively) by using EP. 

Examples of extrapolation parameters are the number of wells for production and storage, the 

number of plants for processing, and gas throughput for all segments of the industry. 

To scale-up a site activity factor to the regional or national level, the site 

activity factor must be divided by the extrapolation parameter for that site and multiplied by 

the regional or national value for the extrapolation parameter. In effect, the site activity 

factor (AF5) is being multiplied by the ratio of two known factors: one on the site basis 

(EP,) and the other at the regional (EP.J level. This is illustrated in the following equation: 

(2) 

Detennining the ratio of the site activity factor to the site extrapolation 

parameter for multiple sites within a region can be done using one of two equations: a siteweighted method or an approach using an average count per site (not site-weighted). The 

first uses a ratio of the summation of activity factors for all sites to the sununation of 

extrapolation parameters for all sites: 

18 
" 

I: AF, 

Method 1: (3) (AF/EP), = i • I 

" (Site-weighted) 

I: EPi 

i • ! 

where n = number of sites. The second is an average of individual site ratios. This 

equation detennines the ratio to be used for scale-up, (AF/EP)s, by summing the ratio of the 

site activity factor to the extrapolation factor, (AF/EP),, calculated independently for each 

site. 

I: 

i=l (AF/EP)s = ::......:_ __ _ 

(AF/EP), 

n 

Method 2: (4) 

(Not site-weighted) 

4.4.1 Selection of Site-Weighted (Ratio) Extrapolation Technique 

The site-weighted method (Equation 3) was selected for the extrapolation 

technique for all production activity factors. In project review meetings, this technique was 

accepted by production industry representatives and their company statisticians as the best 

approach. The site-weighted approach is based on the assumption that the sites are randomly 

sampled, so that a large site is proportionately representative of a large section of the 

population. The site-weighted method, which will hereafter be referred to as the ratio 

method is discussed in greater detail in Appendix B. 

The non-weighted approach was initially used in earlier drafts of this report as 

the extrapolation method. It was based upon an assumption that the "representativeness" of 

any site could not be determined. It assumed that a small sampled site (15 wells) may 

actually be part of a heterogeneous large field (!0,000 wells), while a large site (100 wells) 

might be the full sample of a small field. However, industry reviewers found that this 

assumption was not the best representation for the production segment, and therefore the 

ratio method was used. The ratio method was also used in the gas processing, transmission, 

and storage segments. 

19 
4.4.2 Extrapolation Parameter Selection 

Some equipment activity factors can be scaled up by any of several available 

extrapolation parameters (EP). These parameters must be known for each site as well as for 

the national or regional total. For example, in the production segment there are only three 

extrapolation parameters: 1) gas well count, 2) gas production rate, and 3) total "gas 

industry" well count. Gas wells are simply the total count of gas wells at the site, and gas 

production rate is the rate of gas marketed from the site. Both of these values are well 

known for the nation and for the regiOn. "Gas industry" wells are the sum of gas wells plus 

the oil wells that market gas (oil-only wells are not counted). This last extrapolation 

parameter is not known exactly, since the regional or national number of oil wells marketing 

gas is not known. Nevertheless, the number of oil wells marketing gas was determined to a 

reasonable degree of accuracy (see Section 5.5). 

If there was a physical or teclmical relation between the equipment and one of 

the three candidates for the extrapolation parameters (EP), then that parameter was selected. 

This was the case, for example, for in-line heaters and chemical injection pumps, which are 

related to well count based on engineering design reasons. However, where the relation 

between the source population and various extrapolation parameters is not definitive from a 

technical perspective, a single EP cannot be selected. Therefore some alternate approaches 

for combining the various EP's can be used, such as: 1) averaging the results from both 

methods of EP extrapolation, or 2) statistically analyzing the data to determine the best 

approach. 

For example, statistical analysis was used in selecting the activity factor 

determination for meter stations in distribution. It was not clear from a ta;hnical perspective 

whether to scale-up the number of metering/pressure regulating stations by miles of main 

pipeline or by system throughput. which were the only known population statistics. The 

station counts from individual companies were examined both on a per mile of main and per 

20 
system throughput basis. A linear regression analysis showed that extrapolating the data 

using a per mile main basis produced less variability in the national extrapolation. 

In the production segment, Radian examined the technical relationship between 

equipment and the three extrapolation parameters (gas well counts, total well counts, or 

production rate). The EP selected was based upon Radian's and the industry reviewer's 

knowledge of production operations. If the equipment was most strongly related to one 

parameter, and not related or very weakly related to the other parameters, then the first 

parameter was selected. If there waS a rationale for a strong technical relation between both, 

then an average of the approaches was used. 

In several cases, the total "gas industry well" count could be excluded as an 

EP if the boundaries selected (see Figure 3·2) excluded that equipment on associated oil 

wells. In many cases, there was a reason to expect that equipment counts were related to 

both wen counts and production rate. This was true for pneumatics, separators, and 

compressors. 

The rationale for relation to both parameters on this equipment is as follows. 

Surface facilities may be organized as central facilities or individual wen site facilities. If 

surface facilities are sited on individual well sites, which occurs frequently, then a relation 

between equipment count and wells would be expected. If the equipment was sited at central 

facilities, then it might be specified and sized based purely upon production rate. Since both 

relations clearly existed, the average was selected. The production activity factors, 

extrapolation parameters, and the basis for the selection are listed in Table 4-2. 

4.5 Activity Factor Accuracy 

Determining the accuracy of the activity factor is a key pan of estimating the 

accuracy of the national emission rate. Accuracy is dependent on precision and bias. as 

discussed in Volume 3 on general methodology! and Volume 4 on statistical methodology .

3 

21 
TABLE 4-2- PRODUCTION EXTRAPOLATION PARAMETER SELECTION 

Activity Factor Extrapolation Pararneter(s) Selected Basis 

In-Line Heaters Gas weHs In-line 

While 

heaters exist only on gas wel!s and have a strong relation to well count. 

within 

some heaters (such as heater-treaters) do exist on oil wells, they are not 

the boundaries of the gas industry (see Figure 3-2). 

Separators Mean of: I) gas well and 2) production rate. Separators can be related to both production rate and well count Separators can 

Exception: offshore, where the relation is to be at individual wells, or can be at central facilities, where production rate may 

production rate only. 

Com 

play a factor. In the offshore area, the relation is stronger to production rate. 

pressors Mean of: I) total gas industry wells and 2) Compressors exist within the gas industry on the overhead line~ from gas· well 

production rate separators and associated-oil well separators. Therefore 

well count. 

there is a relation to total 

There is also a relation to production rate since there are size limits 

for compressors. 

Pneumatics Mean of: I) total gas industry wells and 2) Pneumatics exist on the overhead gas lines from associated oil wells and from 

production rate 

compressors 

gas wells. Gas lines from any gas producing well may have dehydrators and 

with pneumatics. Therefore pneumatics are related to total gas 

where 

industry well count. Pneumatics can also be related closely with production rate 

central separation facilities exist. (The siting of equipment and pneumatics 

at central facilities can be largely dependent on production rates.) 

Chemical Gas wells CIPs were found mostly at individual well sites, even where central separation 

Injection Pumps facilities existed. C!Ps therefore have a strong relation to well count. While 

(CIP') C!Ps do exist on oil wells, oil well CJPs are not within the boundaries of the gas 

industry (see Figure 3-2). Therefore gas well count was used. 
Precision, the random invariability in the measurement, is calculated by propagating error 

from each individual group of measurements. However, bias, a systematic error in the 

estimate, must be discovered and eliminated. 

4.5.1 Precision 

If the activity factor estimate is assumed to be approximately normally 

distributed, then the 90% confidence limits for the activity factors determined by the siteweighting method can be estimated using Cochran's4 equation 6.14. The equation for the 

90% confidence interval (symmetric) for the site-weighted activity factor is: 

± t(l-a:/2,n-l) .jU 

The equation for the variance is: 

where 

= N'(l-f) v 

n(n-1) 

n 

E (y,-Rx)' 

i=l 

Yi = the number of equipment at site i in the sample set; 

xi = the number of wells or amount of production at site i in the sample 

set; 

n = number of sites sampled in the given region; 

N = the total number of sites in the region; 

f = sampling fraction = n!N; 

R = activity factor ratio = (AF/EP)samp1

e; and 

t0 _a12• n-1} = the 1-a/2 probability of the Student's t Distribution with n-1 

degrees of freedom. 

23 

(5) 

(6) 
The total number of sites (N) is not known for each region or nationally. Thus, it must be 

estimated by the following equation: 

N= (Production or Wells)total,regton 

(Production or Wells)10tal.sample jn 

(7) 

Either production rate or wells can be used in the equation, depending on which extrapolation 

parameter is used. 

4.5.2 Propagation of Error 

This section discusses the general techniques used to propagate the error 

bounds {for precision) that are calculated in Section 4.5.1. The error bounds of two numbers 

can be propagated to determine the error bound of their sum and/or of their product. These 

techniques are covered in more detail in Volume 4 on statistical methodology,3 but are 

summarized here for the reader's convenience. Products are often used in this study since 

the basic extrapolation technique was to take the product of AF x EF to obtain the source's 

emission rate {see Section 2). Sums are also used frequently since all of the individual 

source emission rates are summed to obtain the national annual emissions from the natural 

gas industry. 

Section 4. 5 .I discussed the calculation of 90% confidence half widths for a 

single term, such as an emission factor or activity factor. These confidence half widths can 

be plugged into the following equations to determine the confidence bounds for sums and 

products. 

For uncorrelated values, the error bound (90% confidence half width) of a sum 

is the square root of the sum of the squares of the absolute errors of the values being 

summed. An illustrative example follows. Suppose the following values, A and B, are to be 

summed, and that the confidence bound of value "A" is expressed as "a" (in absolute terms). 

The bottom cell of Table 4-3 shows the resulting error calculation for the sum. 

24 
TABLE 4-3. ERROR PROPAGATION FOR A SUM 

90% Confidence Half Widths 

Values to be 

Summed Absolute Value Percent Value 

A a a%= 100 X a/A 

B b bo/o= JQQ X b/8 

Sum = (A + B) absolute error of sum = square root of (az + b:) 

For correlated values, the equation for error becomes (a2 + b2 + 2rab )

112 , where r = 

correlation coefficient between A and B. However, r was assumed to be zero for most 

categories since they were derived from different data and were unrelated. 

The error bound (90% confidence half width) on a product is also calculated 

with the absolute errors of the tenns being multiplied. Suppose that A X B = C, and that 

the errors for A and B are expressed as a and b, respectively. The errors expresed as a 

fractional value would be fa and fb, respectivley. The bottom cell of Table 4-4 shows the 

resulting error calculation for the product. 

TABLE 4-4. ERROR PROPAGATION FOR A PRODUCT 

90°/o Confidence Half Widths 

Values to be 

Multiplied Absolute Value Fractional Value 

A a fa= a/A 

B b fb= b/B 

Product = A x B relative error of product = square root of [fa2 + tb2 +(fa2 x fb2

)] 

25 
4.5.3 Screening for Bias 

It is impossible to prove that there is no bias in any data set. While tests can 

be designed that are capable of revealing some bias, there are no tests nor group of tests that 

will reveal ali possible biases. Assuming that a data set has no bias, even after extensive 

testing, is only a hypothesis. Such hypotheses can be disproved, but cannot be proven. 

However, the data collected during this project were carefully checked and rechecked to 

eliminate all identifiable biases. Three basic methods were used to screen for bias: peer 

review of the activity factors by expei-ienced sources, analysis of the data, and extrapolation 

by different parameters (EPs). 

Methods for gathering the data were tested repeatedly through extensive 

technical and industrial review. Numerous individual reviews and project advisor's meetings 

over a number of years were used to examine the project data with industry representatives 

and other experts so that systematic errors could be identified and eliminated. When possible 

biases in the activity factor samPling plan or extrapolation method were theorized; the project 

was altered to test for that bias and eliminate it if it existed. One example of this review 

process is the identification of regional differences in production practices. These differences 

were identified during the advisor meeting review process. The regional bias was then 

overcome by subdividing the production data into two offshore and four onshore regions, 

sampling within each region, and extrapolating by region. 

Some of the production data that were collected and analyzed were also 

checked for bias by extrapolating the activity factors with multiple parameters (i.e., EPs)_ 

For a subset of data that is perfectly representative of the gas industry, equipment counts 

from the data set could be extrapolated to national totals by any variable in the data set. Any 

extrapolation from the perfect subset of data would deliver the correct answer, regardless of 

the parameter used. For an imperfect data set, which all data sets are, extrapolation by 

multiple variables allows for a cross check for bias. For example, in production, the 

equipment counts can be extrapolated by production rate or well count. These two methods 

26 
often produced different answers that were averaged to minimize the potential bias from a 

single method. 

27 
5.0 PRODUCTION SEGMENT 

Some of the production segment activity factors were well-known, published 

figures, but most production activity factors had to be developed. Production activity factors 

were extrapolated from observed equipment counts at sampled sites based on the only three 

nationally known extrapolation parameters: total gas well count, gas industry well count, 

and gas production rate. Any scaled-up site data were extrapolated using the ratio of site 

wells to national wells or using the ratio of site production to national production. 

5.1 Development of a Regional Approach 

The extrapolations were performed on a regional basis since regional 

differences were known to exist and because each of the well-known activity factors (i.e., 

wen count and production throughput) were also known on a regional basis. Six regions 

were selected based upon an analysis of the production and well population centers in the 

United States as well as upon known differences in practices in various regions. The regions 

are: Gulf Coast Onshore, Gulf Coast Offshore, Central Plains (onshore), Atlantic and Great 

Lakes (onshore). Pacific and Mountain (onshore), and Pacific Offshore. Figure 5-1 shows 

the major producing states and Figure 5-2 shows the regions selected and which states are 

included. 

The differences in the regions justify their selection and can be seen in Table 

5-1. Specifically, Table 5-1 shows the regional differences that exist in production versus 

well count. Each region has a unique oil well versus gas well split and has a unjque 

production rate per well. Two offshore regions exist to account for the known differences in 

practices between onshore and offshore production operations. The well and production 

demographics also support this split since the offshore regions account for a small portion of 

the wells (!.5% of the gas wells and !.2% of the oil wells) but produce 27.7% of the U.S. 

marketed gas production. 

28 
States with Production 

>50,000 MMscf/yr 

Figure 5-1. Major Producing States 
w 

0 

P.M. Offshore 

G.C. Offshore 

Figure 5~2. Selected Production Regions 
TABLE S-1. REGIONAL DIFFERENCES IN PRODUCTION RATES AND WELL COUNTS 

Regional Groupings 

Gulf Coast Region Total 

GC Offshore 

GC Onshore 

Central Plains (onshore) 

Pacific and Mountain Tota'l 

PM Offshore 

PM Onshore 

Atlantic and Great Lakes 

(onshore) 

TOTAL U.S. 

'Table 3·17, Gas Facts1 

b Natural Gas Annuals 

States in Region 

that are 

>50 Bscfy 

TX,LA,FL 

OK,AR,CO,MO,NM, 

WY,KS 

UT,CA,AK 

PA,MI,OH, 

wv 

1992 Producing Gas 1992 Producing Oii 

wens• Wells" 

Percent of Percent of 

Count Total Count Total 

63667 23.1 217567 36.1 

4021 1.5 5140 0.9 

59646 21.6 212427 35.3 

80924 29.3 199103 33.1 

2266 0.8 46722 7.8 

65 0.0 2040 0.3 

2201 0.8 44682 7.4 

129157 46.8 138805 23.0 

276014 100.0% 602!97 !00.0 

1992 Marketed 1992 Gross 

Production"' Production" 

Percent of Percent 

Marketed of Gross 

Bscfy Total Rscfy Total 

11514 61.5 12272 55.4 

5000 26.7 5045 22.8 

6514 34.8 7227 32.6 

5424 29.0 5672 25.6 

984 53 3392 15.3 

186 1.0 279 13 

798 4.3 3113 14.1 

790 4.2 796 3.6 

18712 100.0% 22132 100.0'% 
In regard to the other regions, as shown in Table 5-1, the majority of narural 

gas production in the United States (61.5% of marketed production) is in the Gulf Coast 

Region. All other regions only account for 38.5% of the production, and the majority of that 

fraction of production occurs in the Central Plains region. However, the split on well count 

is completely different. The Atlantic & Great Lakes region, which accounts for only 4.2% 

of the marketed national gas production, has the largest portion of gas wells (46.8% of the 

national total) as well as a large fraction of oil wells (23%). 

If wells or equipment associated only with wells (such as heaters and chemical 

injection pumps) are the sources being evaluated, then bias would potentially be introduced if 

sampling was performed only on wells in the Gulf Coast region, where most of the gas is 

produced. The bias is avoided if the sources are combined regionally, and the regional 

averages should then be added in the same proportion that they are distributed in the actual 

population. 

Table 5-2 shows the relative representativeness of the sample data set for the 

region that it represents. The percentage of site samples (site visits and surveys) in the 

region can be compared with the actual percent of national well count and national 

production that the region represents. The sample set does not exactly match the actual 

regional averages for production per well. The sample sites have a higher production per 

well than the actual region, probably due to lower participation of sites with very low 

production rates. (Lower production rates may be tied to fewer resources and less ability to 

participate in surveys.) 

However, this bias has not caused significant error in the data set since the 

extrapolation technique for activity factors does not use the production rate per well. 

Instead, the extrapolation technique uses both well count and production rate independently. 

Therefore, the high production rate per well should not introduce any error into the final 

estimates. 

32 
TABLE 5-2. SAMPLE SET COMPARISON TO ACTUAL REGIONAL DATA 

Sample Data Set• 

Production Rate 

per Gas Industry 

Number Per<'ent of Total Well (MMcfd) 

of Site Site Samples 

Regional Groupings Samples from tbis Region Marketed Gross 

Gulf Coast Region Total 13 26.0 

GC Offshore 4 8.0 1.66 1.66 

GC Onshore 9 18.0 0.54 0.54 

Central Plains (onshore) 7 14.0 0.22 0.22 

Pacific and Mountain Total II 22.0 

PM Offshore 2 4.0 5.22 5.22 

PM Onshore 9 18.0 0.12 0.32 

Atlantic and Great Lakes (onshore) 19 38.0 0.018 0.019 

TOTAL U.S. 50 100.0 0.14 0.16 

• Includes Radian site visits (24), Radian phone surveys (15), and Star site visits (12) 

b Table 3-10, Gas Facrs1 

< Narural Gas Annual5 

1992 Actual Regional Data&,c 

Production Rate 

per Gas Industry 

Percent of Percent of Percent of Well (MMcfd) 

Total Oil Total Gas Marketed 

Wells Wells Gas Total Marketed Gross 

36.1. 23.1 61.5 

0.9 1.5 26.7 2.36 2.38 

35.3 21.6 34.8 0.13 0.15 

33.1 29.3 29.0 0.10 0.10 

7.8 0.8 5.3 

0.3 0.0 1.0 0.66 0.99 

7.4 0.8 4.3 0.12 0.48 

23.0 46.8 4.2 0.012 0.012 

100.0 100.0 100.0 0.!06 0.125 
5.2 Gas Well Count and Production (Onshore and Offshore) 

Gas well counts for 1992 were available from several sources (Gas Facts, 

Table 3-17,' or from World Oil,6 or from DOE EIA's Natural Gas Annual'). These sources 

give total U.S. gas wells for each year. The data are also sorted by state, which allowed 

regional groupings to be made, as was done in Table 5-1. 

The split of counts into offshore and onshore weiis has to be made for the 

states that have offshore production in state waters~ these states are Alabama, California, 

Louisiana, Texas, and Alaska. World Oil (Feb 93) gives figures for offshore gas well counts 

for Alabama, California, and Louisiana. Texas and Alaska data were not split. Texas data 

were obtained from contacts with the Texas Railroad Commission (RRC). 7 The Alaska 

offshore count was provided by the Alaska Oil and Gas Conservation Cornmission.8 

Gas w~Il production data for 1992 were also available from several sources 

(Gas Facts. Table 3-17,2 or from the U.S. Department of Energy (DOE) EIA's Natural Gas 

Annual'). These sources give total U.S. gas wells for each year. The data are also sorted by 

state which allowed regional groupings to be made, as was done in Table 5~1. Production 

was sorted into onshore and offshore production using DOE EIA's Natural Gas Annual, 

Table 4, "Offshore Gross Withdrawals of Natural Gas By State, 1992." 

5.3 Oil Well Count and Production (Onshore and Offshore) 

Oil well counts for 1992 were available from the Oil and Gas JoumaP and 

from World Oil (Feb 1993).6 Similar to gas wells, the onshore and offshore split of oil wells 

was made by World Oil for Alabama, Alaska, California, and Louisiana. Texas data were 

not split. Again, Texas offshore oil well data were obtained from contacts with the Texas 

Railroad Commission. 7 

34 
Gas production from oil wells for 1992 was also available from DOE EIA's 

Natural Gas Annual5• These sources give total U.S. gas wells for each year. The data are 

also sorted by state, which allowed regional groupings to be made, as was done in Table 5-i. 

Production was sorted into onshore and offshore production using DOE EIA 's Natural Gas 

Annual, Table 4, "Offshore Gross Withdrawals of Natural Gas By State, 1992." 

5.4 Oil Wells Marketing Gas 

Oil wells that produce natural gas and sell the gas into the natural gas system 

have been considered a pan of the natural gas industry for the purposes of this report. The 

wellhead is not considered part of the gas industry, but downstream gas production 

equipment associated with these wells is included. However, oil wells that do not produce 

gas, or that produce gas that is vented or consumed on site are not considered p<irt of the gas 

industry. Methane emissions from this latter type of oil well are not included in our 

estimates. 

There are no national statistics on the fraction of oil wells that market gas. 

Only the total oil well count is known. Even differentiation of gas wells from oil wells are 

based on a gas-to-oil ratio (GOR) and may vary state-to-state. The definition in Texas for an 

oil well is any well that has a GOR lower than 100,000 cubic feet (of gas) per barrel (of oil). 

A GOR greater than 100,000 qualifies the weii as a gas well. 

Since there are no national statistics on oil wells that market gas, the national 

statistics on total oil wells had to be manipulated to produce this number. To determine the 

number of oil wells that produce gas which is not marketed, detailed state data from 

Louisiana, Colorado, California, and Texas were examined for disposition designations that 

would indicate whether gas from the wells was marketed or internally consumed. Only 

Texas data allowed such a differentiation. 

35 
Texas RRC magnetic tapes for 1989's P-1 and P-2 disposition repons were 

purchased, downloaded, and analyzed with a database management program. 10 Oil wells are 

not tracked on an Individual well basis but are tracked on a lease basis, so the data will be 

discussed on a lease basis. The analysis showed that oil wei! leases with some marketed gas 

account for only 34.7% of all oil well leases. This figure was obtained by taking the mean 

of the percentages for each month's worth of data. The minimum observed monthly value 

was 28.0% (March !989), and the maximum was 46.7% (October 1989). The standard 

error, used in the calculation of statistical confidence limits, was 2.8 percentage points, or 

7.8% of the mean value. The reader should note that this error demonstrates the variability 

of the Texas data and has been assumed to be equal to the variability of the national data. 

Dwight's Energy Data, Inc. was asked to query their database to detennine the 

fraction of oil leases that were producing gas for sale in Oklahoma. 11 They found that 52% 

of the oil leases in Oklahoma had sold gas. However, Dwight's was unable to confirm what 

GOR was used to differentiate between an oil lease and a gas lease. Thus, the 52% was not 

used in the calculation of associated gas wells, but was thought to confinn the magnitude of 

the 34.7% value that was obtained from the RRC tapes. 

Total gas industry well counts were determined by multiplying known regional 

oil well counts by 0.347, and adding these oil wells to all of the gas wells. These 

calculations were perfonned on a regional basis, as shown in Table 5-3. The total gas 

industry well counts were used to eXtrapolate populations of chemical injection pumps, inline heaters, separators, and compressors as detailed in Section 5. 7. 

5.5 Offshore Platforms 

A separate method for detennining offshore platfonn count was necessary 

because offshore production fugitive studies produced emission rates per platform, rather 

than per equipment. While published data are available on the number of active drilling rigs, 

no published data were found on the number of producing platforms. Two separate sources 

were contacted to obtain the required information. 

36 
TABLE 5-3. NATIONAL GAS INDUSTRY WELLS 

Oil Wells 

Gas Wellsb Oil Wells' Marketing Gas• 

Region (1992 data) (1992 data) (1992 data) 

Gulf Coast Offshore 4021 5140 1780 

Gulf Coast Onshore 59646 212427 73700 

Central Plains (onshore) 80924 199103 69100 

Pacific Offshore 65 2040 708 

Pacific & Mountain On"hore 220l 44682 15500 

Atlantic & Great Lakes (onshore) 129157 138805 48200 

TOTAL U.S. (l992 data) 276014 602197. 209000 

• Based upon analysis of Texas RRC data that shows 34.7% of all oil wells market gas.7

· 10 

b Source on well counts: Gas Facts. 2 

± 90% 

Confidence 

Limits 

(absolute) 

133 

5479 

5135 

53 

I 152 

3580 

15533 

± 90% 

Total Gas Confidence 

Industry Wells Limits 

(1992 data) (%of Mean) 

5800 2.3 

133000 4.1 

15000 3.4 

773 6.5 

17700 6.5 

177000 2.0 

485000 3.2 
The first source for platform count information was Offshore Data Services, 

Inc., 12 which tracks statistical data on offshore production. They estimated that in 1993 there 

were 1607 producing platforms in the Gulf of Mexico, and 45 platforms in the rest of the 

United States. This includes platforms in state waters. 

The second source for platform data was the Minerals Management Service 

MMS Outer Continental Shelf Activity Database (MOAD). 13 This database provided a count 

of 1,857 producing platforms in the Gulf of Mexico (excluding platforms located in state 

waters). However, since MOAD only has an 85% response rate from industry, the number 

was extrapolated to 2,185. MOAD does not account for platforms located in state waters in 

the Gulf. 

The MOAD number could be overestimated since it counts some platforms with no 

production, and could be underestimated since platforms in state waters were not included. 

However, since the Offshore Data Services number was lower, the MOAD count was likely 

not underestimated. The MOAD data were used for the Gulf of Mexico platform count 

because they were conservatively high and could be examined more readily. The sum of the 

extrapolated MOAD Gulf of Mexico count and the Offshore Data Services count for the rest 

of the United States was used in the study. 

The total platform count was split 50/50 between "oil" industry and "gas" 

industry platforms. No data were available to defme the actual split, but industry reviewers 

approved this approach. The total counts for the two regions and the split of gas industry 

platforms are shown in Table 5-4. 

The total offshore oil and gas well count is 11,300 (see Table 5-3). The 

result, when divided by the total platform count, is 5.1 wells per platform. Site visit data on 

platforms pr<Xiuced a ratio of 5 to 10 wells per platform. This ratio validates the relative 

magnitude of the platform counts provided by Offshore Data Services and the MOAD 

38 
TABLE 5-4. PLATFORM COUNTS 

Total Gas Industry 

Region Platform Connt Platform Count 

Rest of U.S. 45 22 

Gulf of Mexico 2,185 1,092 

Total U.S. Offshore 2,230 1,!14 

database. Therefore, the 90% confidence interval was assigned to be ± 10% based on 

engineering judgement. 

5.6 Specific Equipment 

Nationally tracked data are not available for most equipment in the production 

segment. Therefore, this study estimated total equipment in the production segment from 

specific counts taken during the study's site visits. A total equipment count for a facility was 

estimated while on site. Then the site equipment counts were extrapolated to regional counts 

with an extrapolation parameter (something that is known both for the site and for the 

region). Section 4.4, Equation 3 discussed the method used for extrapolation. 

For major equipment activity factors in the production segment, equipment 

counts could be extrapolated by either active wells or marketed production for each site. As 

explained in Section 4.4.2, the active wells extrapolation parameter can be either gas only 

wells or total "gas industry"' wells. "'Gas industry"' wells include gas wells and oil wells that 

also market gas. These three extrapolation methods (gas wells, total wells, and production 

rate) can yield very different results. Therefore, technical relations were used to select the 

appropriate extrapolation parameter(s), as was explained in Section 4.4.2, Table 4-3. 

The extrapolations were also performed on a regional basis in order to account 

for regional differences in the key parameters such as operating practices, gas quality, well 

39 

• 
• 

production rates, equipment design, and field ages. The site data were organized into 

regions, and regional ratios were then detennined. The regional ratios were then multiplied 

by the regional count of active wells (gas and total wells) or by regional marketed production 

to obtain a total count of equipment in the region. Regions were added together to determine 

the national number. The final national equipment counts are summarized in Table 5-5. 

TABLE 5-5. SPECIFIC EQUIPMENT ACTIVITY FACTORS 

Activity Factor Count and Confidence Limits 

Separators 167.000 ± 28% 

Heaters 

51,000 ± 95% 

Pneumatics 249,000 ± 48% 

Chemical Pumps 17,000 ± 143% 

Compressors 17,100 ±52% 

The results of the extrapolations for separators, in-line heaters, pneumatics, 

chemical injection pumps, and compressors are presented in Tables 5-{) through 5-10. The 

last column in Tables 5-6 through 5-10 presents the selected activity factor value. The site 

equipment count data used in developing the regional counts ·in Tables 5-6 through 5-10 are 

presented in Appendix A. 

5.7 Vessels and Pressure Relief Ya!ves 

Specific equipment activity factors were combined and used to extrapolate 

emission rate estimates for vessel blowdowns and pressure relief valves (PRVs). The 

number of production "vessels" (used in estimating vessel blowdown emissions) was based 

on the sum of separators and in-line heater counts from Tables 5-6 and 5-7 and the number 

of dehydrators (discussed in Section 8), resulting in 256,000 vessels. Compressors were not 

included in the vessel activity factor since emission factors for compressor start-ups and 

blowdowns were treated separately. A confidence interval of± 26% was determined by 

40 
TABLE S-6. SEPARATORS IN PRODUCTION 

Arith. Mean 

Gas Well Basis Marketed Gas Basis (Gas and Mkt.} 

Wt. Mean Final Total Activity Factor E~rapolated .... Activity Factor Extrapolated .... Equipment 90% Extrapolated 

~umatic Devices Well Basis Equip/Gas Well CoW>I eo..r Equip/Mkt Gas Count Coni ' Count Coni Count 

NATIONWIDE Basis {non-regional) No< 0.727 200,566 4. !82 214.399 

Applicable 

NATIONWIDE Sum by Region 200,312 26% 134558 36% 167,172 

Gulf Coast Offshore Region 0.335 1,349 129% 0.060 825 42% +I· 

Gulf Coast Onshore Region 0.985 58,763 23% 0.93! 16.614 48% -0.3!0 37,689 17% 28% 

Central Plains Region 0.486 39,335 119% 2.144 3!,865 123% 0.868 35,600 117% 

Pacific/Mountain Offshore Region 0.083 5 300% 0.006 3 101% 

Pacific/Mountain Onshore Region 0.849 1,869 300% 0.412 902 268% 0.700 1,385 271% 

Atlantic/Great Lakes Region 0.766 98,991 17% 38.972 8049 32% 0.859 91.670 23% 
TABLE 5-7. IN-LINE HEATERS IN PRODUCTION 

G8s WeU Bam Marketed Gas Ba!ris 

Final 

Total WeD Activity Factor Extrapolated 90% Acthity factor Extrapolllted 90% Extrapolated 

In-Line Healer$ Basis Equip/Gas Well Count Co»l EqWp/Mkt Gali Count Co»l 

NATIONWIDE Basis (non-regional) 

' Count 

No< 0.282 77,865 Not Applicable 

NATIONWIDE Sum by Region 

No< 

Applicable 

51,000 95% Applicable 

Gulf Coast Offshore Region 

51,000 

0000 0 O% 

Gulf Coast Onshore Region 

+I· 

0.224 13,342 121% 

Cemral Plains Region 

95% 

0.435 35,!97 128% 

Pacific/Mountain Offshore Region 0.000 0 0% 

Pacific/Mountain Onshore Region 1.000 2,201 JOO% 

Atlantic/Grear Lakes Region 0.002 260 196% 
TABLE 5-8 . PNEUMATIC DEVICES IN PRODUCTION 

Arith. Mean 

Total WeU Basi.oi Marketed Gas Basis (Gas and Mkt.) 

Activity 

WI. Mean Final 

Pneumatic Devices 

Factor Extrapolated Gas Well Activity Factor Extrapolated 90% Equipment 90% Extrapolated 

Equip/Gas Well Count 90% Conf Basis Equip/Mkt Gas Count Conf ' Count Conf Count 

NATIONWIDE Basis (non-regional) 0.5!1 250.958 3.458 177,253 

NA TIONWrDE Sum by Region 353,356 56% 144,867 35% 249.111 4&% 249,11 I 

Gulf Coast Offshore Region 0.127 739 224% No< 0.077 1,049 26% -0. !02 894 92% +I 

Gulf Coast Onshore Region 0.575 76,649 64% Applicable LOW 18,917 69% 0.930 47,783 64% 4&% 

Central Plains Region I .325 198,890 94% 4.947 73509 42% 0.950 136,199 80% 

Pacific/Mountain Offshore Region 0.000 0 0% 0.000 0 O% 0.000 0 0% 

Pacific/Mountain Onshore Region 0.080 1,423 131% 0.666 I ,455 249% 0.703 1,439 222% 

Atlantic/Great Lakes Region 0.427 75,654 54% 23.072 49,937 77% 0.950 62,796 62% 
TABLE 5-9. CHEMICAL INJECTION PUMPS IN PRODUCTION 

Gas WeU Basis 

Arith. Mean 

Marketed Gas Basis (Gas and Mkt.) 

Wt. Mean Final 

TotaJ Activity Factor Extrapolated .... Activll)' Factor Extrapolated .... Equipment 90% Extrapolated 

CMmical Injection Pumps Well Basis Equip/Gas Well Cowrt Coot Equ1p1Mkt Gas Count Cool ' NATIONWIDE Basis (non-regional) 

Count Cool Count 

No< 0.036 9,903 Not Applicable 

NATIONWIDE 

Not Applicable 

Sum by Region Applicable 

16,971 !43% 

Gulf Coast Offshore Region 

!6,97! 

0.000 0 0% 

Gulf Coast Onshore Region 

+I· 

0.033 1,962 142% 

Central Plains Region 

!43% 

0.159 12,857 184% 

Pacific/Mountain Offshore Region 0.000 0 0% 

Pacific!Mountain Onshore Region 0.679 1,495 300% 

Atlantic/Great Lakes Region 0.005 6S7 48% 
TABLE 5-10. COMPRESSORS (NON-GAS L!Ff) IN PRODUCTION 

Arith. Mean Total WeU Basis Marketed Gas Basis (Gas and Mkt.) 

WI. Mean Final Compressors Activity Factor Extrapolated Gas WeD Aclivlly Factor Extrapolated .... Equipment 90% Extrapolated (excluding gas lift) Equip/Gas WeU Cowrt 90% Conf Basis Equip/Mkt Gas Count Com ' Count Com Count 

NATIONWIDE Basis (non-regional) 0.067 32,317 0.!95 9,977 

NA TIONW!DE Sum by Region 26,000 59% 8,223 41% 17,111 52% 17,1 !2 

Gulf Coast Offshore Region 0.000 0 0% Nol 0.000 0 0% 0.000 n 0% u 

Gulf Coast Onshore Region 0.067 8,991 129% Applicable 0.124 2,219 119% 0.950 5,605 126% 52% 

Central Plains Region 0.095 14,189 70% 0.347 5,157 38% 0.157 9,673 54% 

Pacific/Mountain Offshore Region 0.167 129 300% 0.013 6 300% 0.700 68 296% 

Pacific/Mountain Onshore Region 0.142 2,512 55% 0.348 761 12!% 0.903 1,637 69% 

Atlantic/Great Lakes Region 0.001 179 37% 0.037 80 23% 0.950 129 33% 
combining the confidence intervals for each equipment type using the square root of the sum 

of the squares approach (discussed in Volume 4 on statistical methodology3). 

An activity factor for the number of PRVs was based on an observed PRV 

count per equipment type as shown in Table 5-11. 

Separators 

Heaters 

Compressors 

Dehydrators 

TABLE 5-11. PRESSURE RELIEF VALVES 

Equipment Type 

PRY Count 

Per Equipment 

2 ± 68% 

I ± 89% 

4 ± 84% 

2 ±53% 

Details of these observations are provided in Volume 8 on equipment leaks. 14 

Using the PRV ratios shown above and the equipment counts reported in Sections 5 and 8, 

results in an estimated 529,400 ± 53% PRVs in production. 

5.8 Gatherin& Pipeline Mil§ 

Total gathering pipeline mileage is not reported or tracked nationally and must 

be estimated. The gathering pipeline mileage was divi<;led into three segments. The first of 

these was gas well gathering pipeline miles, and the second was miles for oil wells that 

market gas. The third segment consisted of gathering pipeline miles owned by transmission 

companies. 

Total miles of gathering pipeline were estimated using site visit data from the 

thirteen production sites shown in Table 5-12. Seven of the thirteen sites provided estimates 

of their total miles of pipeline. The fifth site's mileage was estimated from a map of its 

pipelines. 

46 
Gas well gathering pipeline miles were determined using data from the thirteen 

sites. The gathering pipeline miles and the gas wells from the sites were each summed. The 

sum of the gathering pipeline miles (1 ,359) was divided by the sum of the gas wells (2,033) 

to determine an average number of pipeline miles per gas well (0.668). This value was 

extrapolated by the nationwide activity factor for gas wells (276,000) to give the nationwide 

activity factor of 184,000 gathering pipeline miles for gas wells. 

Gathering pipeline mileage for oil wells that market gas was based on the gas 

well mileage data. It was assumed that one half of the gathering pipeline mileage at an oil 

weii that markets gas was in the oil industry, and half was in the gas industry (see Figure 

3-2). Therefore, the average number of pipeline miles per oil well marketing gas was 

divided by two to detennine the average number of pipeline miles per associated gas well 

(0.334). This average was extrapolated by the nationwide activity factor for these wells 

(209,000) to give the nationwide activity factor of 70,000 gathering pipeline miles for oil 

wells that market gas. 

The third segment, gathering pipeline miles owned by transmission companies, 

is reported by A.G.A. to be 86,200 miles (Table 5-12). Utility-owned pipelines are assumed 

to be part of the total production gathering pipeline miles, and were not included in the 

mileage counts in the production site visit data. 

The total production gathering pipeline miles from gas wells, from oil wells 

that market gas, and from transmission companies was summed. This sum gives a 

nationwide activity factor of 340,200 for production gathering pipeline miles. A rigorous 

determination of the 90% confidence interval gave an error less than ± 4%. This value was 

_considered to be too low based on the quality of the data that were used to generate the 

activity factor. Thus, a 90% confidence interval of ± 10% was assumed based on 

engineering judgement. 

47 
TABLE 5-12. PRODUCTION GATHERING PIPELINE MILEAGE- GAS WELLS 

Gathering 

Site Miles Number of Wells 

Site I 46.3 80 

Site 2 8 26 

Site 3 40 130 

Site 4 15.4 12 

Site 5 II 6 

Site 6 5.2 193" 

Site 7 600 1000 

Site 8 441.3 425 

Site 9 0.7 I 

Site 10 27.7 24 

Site II 2.1 3 

Site f2 7.1 7 

Site 13 154.2 126 

TOTAL 1359.0 2033 

• Includes 55 oil wells. 

5.9 Maintenance Activities 

Emission rate estimates for well workovers and completion flaring were based 

on annual counts of a specific activity, as opposed to an equipment count. Completion 

flaring occurs immediately following the drilling process for a new well, where the gas is 

flared to detennine the available pressure and flow rate. Most completion fl~ring is 

associated with exploratory wells to allow proper sizing of meters and surface equipment. 

(The production rates for new wells in existing fields can usually be detennined before the 

well is completed.) The Energy Information Agency (EIA)" estimated 844 wells are 

48 
completed each year, based on 1987 data. A confidence interval of 10% was assigned to this 

value based on engineering judgement. 

Well workovers arc another type of maintenance activity. During a well 

workover, the robing is pulled from the well to repair tubing corrosion/erosion or other 

downhole equipment problems. A study performed by Pipeline Systems Incorporated (PSI) 

reported the number of gas well workovers for two sites. 16 PSI data showed I workover/yr 

per 21 gas wells at Site 1, and 1 workover/yr per 50 gas wells at Site 2. The annual average 

for these two sites is 0.0338 ± 258%- workovers/gas well. (Workovers for oil wells are 

outside the natural gas industry boundaries defined for this report.) The confidence interval 

for this value was calculated using Equation 5. Multiplying this ratio by the total number of 

gas wells (276,014 ± 5% from Table 5-1) results in the number of well workovers per year: 

276,014 gas wells X 0.0338 workovers/yr-well = 9,329 workovers/yr 

A confidence interval of 258% was calculated based on propagating the confidence intervals 

for the terms in the equation above. 

A third type of maintenance, venting or gas well unloading, requires an 

activity factor for the number of gas wells that require unloading. Gas wells operated under 

low pressures can accumulate water in the well bore due to their low flow rate. This water 

chokes the flow of the well, reducing the gas production. To clear the water, the well is 

blown to a tank at atmospheric pressure, where the gas is vented. Data from 22 production 

site visits indicated that 41% (± 45%) of gas wells require this unloading practice to 

maintain production. Additional details on this particular activity factor can be found in the 

Volume 7 on blow and purge activitiesY Multiplying the total number of gas wells 

nationally (276,014 ± 5%) by this value results in 114,139 ± 45% gas wells requiring 

unloading each year. 

49 
5.10 Compressors and Dehydrators 

Activity factors for compressors and dehydrators were calculated in a single 

extrapolation procedure for all segments of the natural gas industry. The details of this 

procedure are discussed in Section 8. 

50 
6.0 GAS PROCESSING SEGMENT 

Activity factors in gas processing are significantly simpler than in gas 

production, since the segment consists of one type of facility: gas processing plants. Major 

activity factors were limited to the number of gas plants (of each type), dehydrators, acid gas 

recovery units (AGRs) and compressors. Ail of these activity factors were either published 

and weii-defined or were developed through other studies such as the Wright Kiiien report 18 

or Volume 11 on compressor driver exhaust. 19 

6.1 Gas Plant Count 

Gas processing plants recover high-value hydrocarbon liquids from natural gas. 

The plants can be divided by process type based upon data from the Oil & Gas Journal 

annual plant survey9 as shown in Table 6-1: 

TABLE 6-1. DIVISIONS OF GAS PROCESSING PLANTS BY TYPE 

Type Count %of Total 

Cryogenic 302 42 

Refrigerated absorption 117 16 

Refrigeration 192 26 

Other 115 16 

TOTAL PLANTS 726 100 

Since the sites visited sampled all of the major types, and since no emission 

factor data were correlated to plant type. only the total plant count was used for the plant 

activity factor. Based upon !992 data from the Oil and Gas Journal, the total gas plant 

count is 726. A confidence limit for the gas plant count was set at ± 2% based upon 

engineering judgement. 

51 
6.2 Acid Gas Recovery (AGR) Units 

Acid gas recovery (AGR) units are used to remove carbon dioxide (C02) and 

hydrogen sulfide (H2S) from natural gas. Absorption of C02 and H2S in aqueous amine 

solutions (e.g., monoethanolarnine, diethanolamine) is the most widely used process for 

AGRs. Some methane is absorbed along with the acid gas in the amine solution and may be 

emitted when the solution is regenerated by heating and decrease in pressure. Purvin & 

Gertz, Inc., estimated the total count of AGRs using an amine process to be 371, with a total 

capacity of 30,433 MMscfd. 20 Most AGRs have flares or sulfur recovery processes that 

prevent the release of methane. However, it is estimated that only 18% of AGRs vent 

regenerator offgas (including acid gas and methane) to the atmosphere. 21 Other AGR 

processes may result in methane emissions but are much more difficult to quantify because of 

the wide variety of processes available. 

6.3 Compressors and Dehydrators 

Activity factors for compressors and dehydrators were calculated in a single 

extrapolation procedure for all segments of the natural gas industry. The details of this 

procedure are outlined in Section 8. 

52 
7.0 TRANSMISSION SEGMENT 

Activity factors for the transmission segment are easier to detennine than 

production segment factors because the transmission segment is more homogeneous than the 

production segment. The transmission segment has only four basic components: pipeline, 

compressor stations, storage facilities, and meter and regulating (M&R) stations. Most 

transmission pipelines are one of two types: cross-country or regional network. 

7.1 Compressor Station Count 

The total number of compressor stations was calculated to be 1700 ± 10%. 

The number was extrapolated from data submitted by major transmission companies to the 

Federal Energy Regulatory Commission (FERC). 22 Pages 508 and 514 of FERC Form 2 list 

the number of compressor stations and miles of pipeline for 46 transmission companies. The 

pipelines owned by these companies comprise 70% of the total transmission pipeline mileage, 

making the sample large but incomplete. The total number of transmission compression 

stations reported in the forms was extrapolated by pipeline mileage to determine the total 

number of compression stations in the transmission segment. A summary of the compression 

station data obtained from FERC Form 2 is presented in Table 7-1. The confidence interval 

of ± 10% was based on engineering judgement. 

In a 1990 survey titled "Natural Gas Industry Environmental Organization 

Structure Survey," A.G.A. surveyed 14 gas transmission companies and found an average of 

138 miles per station. 23 The number of miles per station corresponds to a higher count of 

2062 compressor stations. However, the 1990 A.G.A. survey was less fonnal, and the 

stations used in the survey also included some gas processing plants; therefore, the 2062 

value determined by the A.G.A. is an overestimate of transmission compressor stations. Its 

relative size does, however, lend credence to the selected activity factor of 1700 stations. 

53 
TABLE 7-1. TRANSMISSION COMPRESSOR STATION COUNTS 

Company Number of Stations Miles of Pipeline 

I 5 1,023 

2 47 9,915 

3 29 6,556 

4 28 3,253 

5 64 4,517 

6 119 11,188 

7 13 4,288 

8 78 10,244 

9 6 495 

10 17 U49 

II 20 4,460 

12 14 1,960 

13 I 203 

14 0 375 

15 5 925 

!6 51 9,725 

17 2 548 

18 7 354 

19 15 1,972 

20 1 . 362 

21 2 552 

22 12 1,457 

23 56 10,973 

24 7 971 

25 48 24,182 

26 42 3,551 

27 0 89 

28 13 799 

29 58 6,532 

30 13 1,718 

31 2 462 

32 48 7,896 

33 2 275 

Continued 

54 
TABLE 7-1. (Continued) 

Company Number of Stations Miles of Pipeline 

34 69 14,666 

35 69 9,386 

36 28 5,819 

37 0 436 

38 43 10,670 

39 20 4,194 

40 19 4,218 

41 0 30 

42 29 7,272 

43 8 549 

44 45 6,093 

45 30 3,224 

46 I 269 

Total 1186 . 199,795 

Extrapolated No. of Stations 1,700 

55 
7.2 Stora~e Station Count 

There are two types of gas storage facilities: liquefied natural gas (LNG) 

storage and underground storage. LNG facilities condense the natural gas at high pressures 

for storage and transportation in refrigerated vessels. Underground gas storage areas 

typically operate to accept excess gas from major gas pipelines during surplus production 

flow periods. The stored surplus gas is reinjected into the pipeline when demand is higher. 

Typically, the net amount of gas to storage for any year is zero~ if there is an actual longterm over-supply. the producing wells will simply be cut back. 

Underground storage areas are unique. Unlike liquid storage facilities, 

expensive tanks are not used. Underground gas storage facilities are typically one of the 

following: old, spent gas fields; salt domes; or aquifers. The storage operator simply injects 

the surplus gas into the underground formation, which effectively "stores" the gas. 

Compression is sometimes necessary to inject the gas into the formation or to return the gas 

to the pipeline. 

Underground (UG) station count data were available from Gas Facts, 

Table 4-5 "Amount of Pools, Wells. and Horsepower in Underground Storage Fields. By 

State. 1990-1992. "2 The number of LNG facilities was also available from Gas Facts Table 

4-3, "LNG Storage Operations in the U.S. as of Dec. 31. 1987."24 The total number of 

storage stations is the sum of the underground stations and LNG facilities as shown: 

UG: 386 (storage stations) 

LNG: + 89 (54 complete plants, 32 satellite plants. 3 import 

tenninals) 

TOTAL: 475 (storage stations) 

This project assigned a ±5% accuracy based upon engineering judgement. 

56 
7.3 Storaee Well Count 

Underground storage facilities have wellheads at the surface similar to 

wellheads at producing gas wells. These wellheads sit above the wellbore's connection to the 

underground reservoir. Each wellhead has components that can emit gas. 

The number of active underground storage wells for 1992 is given in Gas 

Facts, Table 4-5, as 17,999.2 This project assigned a± 5% accuracy since this number is 

tracked annually by A.G.A. 

7.4 M&R Station Count 

Meter and regulating (M&R) stations are used to measure flow of gas at a 

custody transfer point, and/or to reduce and regulate the pressure and flow into a 

downstream pipeline system. Some metering facilities do exist within compressor stations, 

but these have already been counted in the compressor station emissions. In the transmission 

segment, separate M&R facilities are usually fenced, above ground facilities that contain 

valves, piping, and meter runs. 

In the U.S. gas industry, most M&R stations exist in the distribution and 

production segments and have already been counted there. M&R facilities counted by the 

transmission companies are generally of the following type: 

1. Receipt from production (meter only), 

2. Delivery to distribution (M&R), 

3. Pressure reduction to inter-company transmission lines with low 

maximum operating working pressure (MOWP), 

4. Interconnects/custody transfer (bi-directional) to another transmission 

company, or 

57 
5. Direct sales (farm taps, direct industrial sales from transmission lines). 

The first three types are already counted in other activity factors as follows: 

• In the production segment, meter runs that transfer the gas to 

transmission companies were included in the well site fugitive 

component count data. 

• Delivery to distribution has been counted in the distribution segment 

M&R stations (see Section 9 for more detail). 

• Pressure reduction stations that reduce the pressure to a line that has a 

lower maximum operable working pressure (MOWP) also are already 

counted in the distribution M&R count (Section 9). 

The two types of M&R stations that have not been included elsewhere are 

transmission company interconnects and direct sales. Most large transmission companies 

have interconnects with other transmission companies to allow for flexibility of supply. 

These shared stations can flow in either direction. The last category (direct sales) is 

comprised of two types: farm taps and direct industrial sales. Direct industrial sales from 

transmission lines are owned by local distribution companies (LDCs), even if they only own 

a few feet of line. Many fann taps are still owned by transmission companies, even though 

there is a trend to let LDCs handle the farm taps or to remove them entirely. 

Transfers to other transmission companies and fann taps were calculated from 

survey data provided by the metering departments of three large (over 10,000 miles of 

pipeline) transmission companies, and from three companies with fewer than 10,000 miles of 

pipeline, as shown in Table 7-2. 

58 
TABLE 7-2. TRAJ'ISMISSION M&R STATION POPULATIONS 

Transfer to 

another Direct 

Company Transmission Co. Farm Taps Industrial Sales Miles of Pipeline 

I 323 23 Confidential 

2 5 0 Confidential 

3 60 0 Confidential 

4 62 48 Confidential 

5 40 3,800 Confidential 

6 0 10,000 Confidential 

Total 490 13,871 658 55,045 (!9.3% of 

U.S. total) 

Total U.S. Activity 2,533 ± 776% 7!,690 ± 787% 938 ± 100% 284,500 

Factor Extrapolated 

by Miles 

Only five of the six companies that responded to the survey reported having 

interconnects with other transmission companies. The activity factor was extrapolated based 

on pipeline miles and was calculated to be 2533 interconnects (transfers). The 90% 

confidence bound was determined to be ± 776%. 

The count of farm taps appears to be extremely regional. Based on 

interviews, it seems that most companies have no farm taps, while others have thousands. 

The activity factor for farm taps was calculated to be 71,690 ± 787%. The calculated 

activity factor is believed to be conservatively high, since only a small percentage of all 

transmission companies have these M&R stations, yet two of the six companies in our data 

set reported a large number of farm taps. 

The activity factor for direct industrial sales was developed from FERC Form 

No. 2, page 306. 22 Industrial sales greater than 50,000 Mcf are listed individually, while 

sales less than 50,000 Mcf are combined into a single item. In the latter case, the total 

amount of gas sold was divided by 25,000 to provide an estimate of the number of sales. 

The national activity factor was extrapolated from the FERC data to give a total count of 938 

direct industrial sales. Due to the uncertainty that this approach introduced to the activity 

59 
factor and to the complexity of retrieving data from FERC, a confidence bound of ± 100% 

was assigned based on engineering judgement. 

7.5 Miles of Pipeline 

The total miles of transmission pipeline equals 284,500 miles for 19922 and is 

available from Gas Facts, Table 5·1. A confidence interval of± 5% was assigned based on 

engineering judgement. 

7.6 Compressors and Dehydrators 

Activity factors for compressors and dehydrators were calculated in a single 

extrapolation procedure for all segments of the natural gas industry. The details of this 

procedure are outlined in Section 8. 

7.7 Pneumatics 

The number of natural gas operated pneumatic devices in the transmission and 

storage segment was calculated based on the average number of devices per station multiplied 

by the total number of transmission and storage stations nationally. Data for determining the 

average number of pneumatic devices per station are presented in Volume 12 on pneumatic 

devices. 25 The total number of transmission and storage stations is the sum of 1, 700 

transmission compressor stations, 386 underground storage facilities, and 89 liquid natural 

gas storage facilities (2,!75). 

60 
8.0 CROSS-SEGMENT ACTIVITY FACTORS 

Many equipment types are u:1ique to a particular segment of the natural gas 

industry. The activity factors from these types of equipment, such as the count of 

distribution customer meters, were determined for each segment and are discussed in detail 

in the Sections 5, 6, 7, and 9. However, a few equipment types are common to all segments 

of the gas industry. Activity factors for these equipment types were determined in one step 

for the entire industry. This section of the report discusses two equipment types, 

compressors and dehydrators. that were determined on an industry-wide basis. 

8.1 Compressors 

Activity factors were developed for compressor horsepower-hours and for 

compressor counts on an industry-wide basis. Compressor horsepower was used as the basis 

for the compressor exhaust calculations, and compressor counts were used as the basis for 

the fugitive and unsteady emission calculations. Compressors exist in all segments of the 

industry except distribution. 

8.1.1 Compressor Horsepower-Hours 

The two main types of compressor drivers in the industry are reciprocating gas 

engines and gas-fired turbines. Two pieces of information are needed to calculate the 

activity factors, expressed as horsepower-hours (hp-hr), for the two types of drivers in each 

segment of the industry except production. These data are the installed horsepower and the 

average annual operating hours. For the production segment, horsepower-hour data were 

available for the activity factor calculation. Table 8-1 presents the necessary parame,ers and 

the resulting activity factors for both engines and turbines by industry segment. This table 

also includes the 90% confidence limits for each factor. 

61 
TABLE 8-1. COMPRESSOR ACTIVITY FACTORS FOR EACH INDUSTRY SEGMENT 

Installed 

Engine 

Installed Annual Annual 

Turbine Hours Hours Engine Turbine 

Production 

Industry Segment MMhp' 

NA 

MMbp• Engine Turbine MMHpiJr MMHp·hr 

NA NA NA 27,460 ± 200% NA 

Processing 4.19 ± 132% b 5.19 ± 99% b 6626 ± 12% 6345 ± 48% 27,760 ± 133% 32,910 ± 121% 

Transmission: 

Compressor Drivers 10.2 ± 10% 4.55 ± 10% 3964 ± 14% 2118±31% 40,380 ± 17% 9635 ± 33% 

Generator Drivers 1.45 ± 23% 0.045 ± 166% 1352 ± 38% 474 ± 620% 1962 ± 45% 21.2 ± 1215% 

Storage 

Compressor Drivers 1.33 ± 14% 0.59 ± 14% 3707 ± 23% 2917 ± 620% 4922 ± 27% 1729 ± 626% 

Generator Drivers 0.085 ± 126% 0.057 ± 184% 191 ± 377% 36 ± 620% 16.3 ± 621% 2.05 ± 1312% 

~ 

b Average 

Does not 

of 

include horsepower associated with gas lift for oil recovery or with electric drivers. 

two estimation methods. 
To determine the horsepower and operating hours for engines and turbines, 

four major sources of data are available: 1) the methane project site visits, 2) the A.G.A. I 

GRI/Southwest Research Institute (SwRI) compressor databases, 3) the Federal Energy 

Regulatory Commission (FERC) Form No. 2 Database, and 4) individual company 

databases. The site visits and company databases were used to determine horsepower and 

operating hours for the processing industry segment and for transmission and storage 

generators. The A.G.A./GRI/SwRI databases were used to determine horsepower for the 

transmission industry segment. Operating hours for the storage industry segment were based 

on data from site visits and company databases. Operating hours for the transmission 

industry segment were based on data from FERC. 22 

The horsepower-hours for the production industry segment were determined 

using data provided by a major natural gas production company. A brief discuss-ion of the 

development of each of the activity factors foliows. (For further details, refer to the report 

Methane Emissions fr~m the Natural Gas Industry. Volume 11: Compressor Driver 

Exhaust. 19) 

Production--The production segment horsepower is based on horsepower-hour 

data from one company for 516 engine drivers. The database included drivers in the Gulf 

Coast (Onshore and Offshore) and Central Plains regions. The total horsepower-hours were 

divided by the company's total gas production before scaling to a national estimate. National 

horsepower-hours were calculated to be 27,460 MMhp•hr using the 1992 marketed gas 

production for the entire United States (Natural Gas Annual, 1992).5 Confidence limits for 

the hp-hr/throughput ratio for the database were calculated to be± 576%. However, this 

value was shown by engineering analysis to be unreasonably high. 19 Therefore, the 

confidence limits on the national estimate were set at ± 200% based on engineering 

judgement. 

A few turbine compressor drivers do exist in the production segment; 

however, the total population is insignificant and was therefore not included in the summary 

63 
calculations. This emission source would be an insignificant source due to the small number 

of these drivers in this industry ·segment and the low methane emission factor for turbines. 

Processing--The p:.ocessing segment horsepower was detennined by 

extrapolating site visit data for ten gas plants. The average of two extrapolation methods was 

use~. The first method extrapolates the site data to a national number by multiplying the 

total U.S. gas plant throughput as of January 1, 1993 (46,510.7 MMcfd, Oil & Gas 

Joumaf) by the site visit horsepower per throughput (47.8 hp/MMcfd for engines and 59.2 

hp/MMcfd for turbines). This gives a total of 2.22 MMhp for engines and 2. 75 MMhp for 

turbines. 

The second method scales up the data by plant count, assuming the visited 

plants are perfectly representative. A national estimate is produced by multiplying the 

horsepower from the site visits by a ratio of 726/10 [the 726 gas plants in the United States 

(Oil & Gas Jouma{) to the 10 sites visited]. This second method, therefore, uses a scaling 

factor of approximately 73 for processing horsepower and assumes that all gas plants have 

approximately the same throughput, despite the variation found in the site data (40 MMcfd to 

750 MMcfd). The activity factors based on the number of gas plants are 6.16 MMhp for 

engines and 7.62 MMhp for turbines. 

To provide a more coruervative estimate, the two methods were averaged 

when calculating the national estimate for processing horsepower. The resulting activity 

factors are 4.19 MMhp for engines and 5.18 MMhp for turbines. 

The annual operating hours were based on the ten site visits and data provided 

from two companies (18 additional sites). For eight of the e!tes visited, typical operating 

hours for each of the compressors were not available. Therefore, all compressors that were 

running during these site visits were assigned annual operating hours of 8760 and an 

compressors that were idle were assigned annual operating hours of 0. An average of the 

average operating hours per site was calculated to get the processing segment operating hours 

64 
(203 engines and 9 turbines). Confidence limits were calculated for the horsepower and 

operating hours estimates from the variation of the data. 

Transmission·· Transmission losses from compressor exhaust have been 

divided into two subsections: compressor stations and storage fields. For each of these 

areas, activity factors were calculated for compressor drivers and for generator drivers. 

Transmission Compressor Stations--The transmission segment horsepower for 

each compressor type was determined using the GRI TRANSDAT database. 26 GRI 

TRANSDAT has not been revised since the 1989 data were collected, and it is assumed that 

the data are valid for the 1992 base year. Installed horsepower was taken from the Industry 

Database module of GRI TRANSDAT (11.2 MMhp for engines and 5.0 MMhp for turbines). 

This horsepower number accounts for about 97% of the gas utility industry installed 16.7 

MMhp reported by A.G.A. for 1989.23 

The horsepower values in GRI TRANSDAT are a combination of transmission 

and storage compressor drivers. GRI TRANSDAT also reports that there were 1.5 MMhp in 

the storage segment in !989. Since GRI TRANSDAT did not give separate values for 

engines and turbines in storage, the ratio of engines to turbines in the overall database 

(11.2:5.0) was used giving 1.0 MMhp for engines and 0.5 MMhp for turbines in storage. 

The overall GRI TRANSDAT horsepower numbers were then adjusted for the 1989 storage 

horsepower numbers to give 10.2 MMhp for engines and 4.5 MMhp for turbines in the 

transmission segments. 

Confidence limits for horsepower could not be rigorously calculated from the 

GRI TRANSDAT database because the installOO horsepower was given by installation, or 

site, and then summed to calculate a national estimate. Therefore, confidence limits were set 

based on engineering judgement. 

65 
The annual operating hours are based on data reported on FERC Fortn No. 2 

for the year 1992. 22 The FERC database did not identify the type of driver (i.e., 

reciprocating engine or turbine). As a result, additional data from GRJ TRANSDAT and one 

transmission company were used to split the FERC hours between the two driver types. 

Confidence limits were calculated for the operating hours estimate from the variation of the 

l'ERC data. 

Transmission Storage Fields--The storage segment horsepower (1.920,441 hp) 

came from Gas Facts Table 4-5 for 1992.2 The split between engines and turbines was 

assumed to be the same as the engine and turbine split found in GRJ TRANSDAT (69 .I% 

for engines and 30.9% for turbines). The horsepower data were gathered by A.G.A. 

through a survey of their member companies. whose production makes up approximately 

96% of total gas industry sales. Confidence limits for horsepower data were calculated 

based on an assumed ± 5% uncertainty for the A.G.A. horsepower and an assumed ± 10% 

uncertainty for the GRJ TRANSDAT horsepower splits. 

The annual operating hours are based on site visit data and a company 

database for a total of eleven storage stations (50 engines and 6 turbines). Confidence limits 

were calculated for the operating hours estimates from the variation of the data. 

Transmission Generators--Generators were found at both compressor stations 

and storage fields. The generator horsepower for compressor stations is based on the total 

installed horsepower at seven sites and data provided by one company for 34 compressor 

stations (35 ,006 hp for engines and 1080 hp for turbines). To extrapolate to a national 

estimate, the horsepower per station was multiplied by the total number of transmission 

compressor stations in the United States (1700, FERC Fortn No. 2). 22 

The generator horsepower for storage fields is based on the total installed 

horsepower for one company with nine storage fields (3 engines and 1 turbine). To 

66 
extrapolate to a national estimate, the horsepower per field was multiplied by the total 

number of storage fields in the U.S. (475; A.G.A. Gas Facts, Section 7.22

). 

Generator annual operating hours were also provided by the same sources 

providing horsepower data. An average of the average generator operating hours per site 

was calculated to determine generator operating hours for compressor stations (87 engines 

and 1 turbine) and storage fields (2 engines and 1 turbine). Confidence limits were 

calculated for the horsepower and operating hour estimates from the variation of the 

site/company data except for the case of generator turbines. Since there was only one data 

point for both compressor station generator turbines and storage field generator turbines, 

confidence limits were assumed to be equal to those calculated for turbine compressor drivers 

at storage fields. 

The final activity factors (Table 8-1), in horsepower-hours, were calculated 

using the national estimates for compressor horsepower and the average operating hours for 

each industry segment except for the production segment. Production horsepower -hours 

were estimated as described above. Activity factor confidence limits were propagated from 

the confidence limits for the individual terms using a standard statistical approach. 

8.1.2 Compressor Counts 

Compressor counts were developed using a variety of sources. National data 

from DOE and site visit data were used to make the estimates. Table 8-2 provides a 

summary of the results and the 90% confidence bounds. 

Production--The compressor counts in the production segment were developed 

from the site data collection and a regional extrapolation that is explained in detail in Section 

5. 7. Confidence bounds were calculated for these estimates from the variation of the site 

data. 

67 
TABLE 8-2- COMPRESSOR COUNTS IN THE GAS INDUSTRY 

Reciprocating Total 

Segment Engines Turbines Compressors 

Production 

Gas 

17,112 ± 52%b 0' 32,327 ± 64% 

Plants 

Transmission 

4,092 ± 47.7% 726 ± 76.6% 4,818 ± 44% 

Storage 

6,785 ± 16.6% 681 ± 16.2% 7,466 ± 15% 

Combined 

930 ± 58.3% 136± 119% 1,066 ±54% 

Transmission & Storage 7,715 ± 10% 817 ± 10% 8,532 ± 9.1% 

Total Industry 44,134 1,543 45,677 

Only 

"gathering 

one production site (out of 27 with data on compressors) had any turbines. However, those 

compressors" actually were physically part of an attached gas plant, and were therefore 

added into the gas plant counts. 

b Non-gas lift compressors 

Along with the count of total compressors used in production, it was also 

necessary to determine an activity factor for compressors at large gathering compressor 

stations. Compressors in transmission and storage stations were found to have blowdown vent 

lines routed to the atmosphere through a separate stack. Because of the lack of a good seal 

on the blowdown valves, these blowdown lines were found to have extremely large 

continuous fugitive emission rates. These emission rates had not been considered or 

measured until the GRI/EPA campaign to measure fugitive emissions from transmission 

compressors that occurred in late 1994. 

Originally, it was assumed that compressors used in production did not have 

these separate vent lines, or that the vent lines were small and located proximate to the 

compressors so that the API/EPA fugitive measurement efforts had included them. 

However, several companies reported that some large gathering compressor stations (that had 

a large number of engines) were similar to transmission stations, and did have these separate 

vent lines to a separate stack. 

Therefore, for production, the FERC22 data were analyzed for gathering 

stations. (Gathering was included in the production segment.) In FERC Fonn No. 2, the 

number of stages of compression operating on the peak demand day is reported. It was 

assumed that there are an average of 3.3 stages of compression per compressor, and that a 

68 
large station would contain at least five compressors. Therefore, a station with 16 or more 

stages of compression was counted as a large gathering compressor station. Based on this 

criteria, three large gathering compressor stations were identified in the FERC data. This 

number was then extrapolated from the 86,200 miles of gathering pipeline mileage owned by 

transmission companies, to the national activity factor of 340,000 gathering pipeline miles, 

for a total of twelve large compressor stations in production. The FERC data also showed 

that the large compressor stations averaged 26 stages of compression, or eight compressors 

per large station. This value was extrapolated in the same manner to provide a national 

activity factor of 96 compressors at large gathering compressor stations. A confidence 

interval of ± 100% was assigned to the activity factor for large stations and for compressors 

at large stations based on engineering judgement. 

Processing-The compressor counts for the processing segment were 

determined from eleven gas plant site vi$ its. The average ratio of compressors per plant was 

multiplied by the 742 plants (see section 6.1) to obtain the values in the table. Confidence 

bounds were calculated for these estimates from the variation of the site data. 

Transmission & Storage--The GRI TRANSDAT industry database was used 

to estimate the total compressor counts." The industry database of 16.2 MMHp bad 7489 

engines and 793 turbines. However, the database is not quite a complete sample of the 

transmission/storage sector since its horsepower is slightly lower than the total 

transmission/storage horsepower of 16.7 MMHp for the year 1992 (as shown in Gas Facts, 

Table 5-5, "Gas Utility Industry Installed Compressor Horsepower, 1968-1993"2

). 

Therefore, the database totals were multiplied by the ratio of the two values (16.7/!6.2) to 

determine the adjusted totals of 7715 engines and 817 turbines. Confidence bounds were not 

calculated but were estimated to be ± 10% based upon the large size of the TRANSDAT 

database and the tight confidence of the Gas Facts data. 

The combined transmission/storage count of storage compressors was divided 

into separate categories (i.e., transmission and storage) based upon site visit data and Gas 

69 
Facts data. Site visits to eight storage sites produced ratios of0.750 turbines per site(± 

119%), 5.13 reciprocating engines per site(± 58.3%), and 13,033 HP/site. The site data 

are assumed to have come from larger than average stations since Gas Facts shows that the 

actual horsepower per underground storage site for 1992 was only 4975 HP/site (Table 4-5, 

"Amount of Pools, Wells, Stored Gas, and Horsepower in Underground Storage Fields, By 

State, 1990-1992"2

). Therefore, the site ratios were adjusted to account for the higher than 

average horsepower by multiplying by 4975/13033. The adjusted ratios were then multiplied 

by the 475 storage compressor stations (see Section 7.2). Transmission compressor counts 

were then determined by subtracting the count of compressors in storage ( 1 066) from those 

determined by the TRANSDAT database analysis. 

8.2 Dehydrators 

Multiple activity factors associated with dehydrators were determined for the 

entire gas industry. These factors are based on industry segment gas throughput, and the 

percentage of units with flash drums, gas pumps, and stripping gas. This study is only 

concerned with glycol-based dehydrators (as opposed to dry-bed) since only the glycol 

dehydrators emit methane. 

8.2.1 Dehydrator Count 

A national count of 41,700 dehydrators in the gas industry was recently 

determined by a 1994 GRI study performed by Wright Killen & Company." The study, 

"Natural Gas Dehydration, Status and Trends," used several states' large survey databases 

[Wyoming, Texas Mid-continent Oil & Gas Association (TMOGA), and the Louisiana 

Department of Environmental Quality (LDEQ)] to determine the count of dehydrators in the 

natural gas industry. The Wright Killen study divided the dehydrators into segments of the 

natural gas industry based on data from the TMOGA and Gas Processors Association (GPA) 

survey. 27 The information in this survey allowed Wright Killen to estimate the counts of 

70 
dehydrators in the different industry segments. These values can be found in the first 

column of Table 8-3. 

TABLE 8-3. U.S. GAS INDUSTRY DEHYDRATOR COUNTS 

Wright-Killen Total Dehydrator 

Counts Glycol Dehydrator Count 

Segment (All types) Based on Adjusted Totals 

Production 25270 37824 

Gas Processing Plants 7923 498 

Transrrtission 8507 201 

Storage NA 1092 

Total Gas Industry 41700 39615 

This GRI/EP A study used the Wright Killen report as the basis for the overall 

count of dehydrators because the report provides a reasonably accurate estimate of the 

industry. The Wright Killen report estimates that there are 7923 dehydrators at 742 plants, 

or 10.6 dehydrators per gas plant. However, in the Wright Kil1en study, the dehydrators 

were split into industry segments using data from a single regional survey; as a result, the 

gas plant dehydrator count is unrealistically high. This study adjusted the Wright Killen split 

based on GR1/EPA's information from 11 gas plants that shows only 1.41 dehydrators per 

plant. Starting with the Oil & Gas Journal annual gas processing plant list, an estimate of 

the count of glycol-based dehydrators (as opposed to dry-bed) was made. All refrigerated 

processes were assumed to use glycol dehydration, and all cryogenic processes were assumed 

to use dry-bed dehydration. Based on these assumptions, the percentage of glycol-based 

units in the industry is estimated as 48.6% (a total of 498 glycol dehydrators). 

Similarly, the Wright Killen estimate produces 8507 dehydrators for 2190 

transmission/storage compressor stations, or 3.9 dehydrators per compressor station. 

GRI/EPA's data show 2 dehydrators for 17 mainline transmission stations and 17 dehydrators 

for 6 storage stations. The Wright Killen split is probably in error since the TMOGA/GPA 

production segment survey has too few small dehydrators and too many large dehydrators to 

71 
represent the true national production totals. This assessment of the data is validated by the 

fact that Texas, where the study was performed, has a higher production rate per well than 

the national average. Therefore, the splits between industry segments may be biased if the 

national percentage of production dehydrators is larger than in Texas. 

This GRI/EPA study also corrected the production total to 37,824 units to 

maintain the total national count estimated by Wright Killen. These corrected industry 

segment counts are listed in the second data column of Table 8-3. 

By using the site visit ratios for 1700 mainline transmission stations, an 

estimate of 201 dehydrators in transmission service was determined. For the 386 

underg~ound storage stations, the site visit ratio produces an estimate of 1092 in storage 

service. 

The Wright Killen study also found that on an industry-wide basis, 95% of the 

dehydrators were glycol-using (as opposed to molecular sieve adsorbers or other types of 

water removal). Based on the 95% value. the total number of glycol dehydrators in the 

production, transmission, and storage segments was calculated to be 39,615. These data 

have confidence limits of± 20% based on Wright Killen's 90% confidence limits. ·An 

dehydrators in this survey are assumed to be active. 

8.2.2 Dehydrator Throughpot 

Emissions from dehydrators are proportional to the gas throughput of the 

dehydrator. Therefore, an activity factor for throughput for each industry segment was 

determined. There is a direct relationship between the estimated count of dehydrators and 

the average throughput per dehydrator. In fact, the Wright Killen study determined the 

production count by dividing known gas production rates by an estimated dehydrator 

throughput. Therefore, the GRI/EPA study used the Wright Killen report as the basis for the 

72 
dehydrator gas throughput used in this project. The throughput numbers selected for this 

project are shown in Table 8-4. 

TABLE 8-4- INDUSTRY SEGMENT GLYCOL 

DEHYDRATOR THROUGHPUT 

Average Throughput with 90% 

Segment Confidence Intervals Basis 

Production 12.4 Tscf/yr ± 62% WK&C (Adjusted Wyoming data) 1

a 

Gas Processing Plants 

Transmission 

8.63 Tscf/yr ± 22% Oil and Gas Joumaf 

1.09 Tscf/yr ± 144% TMOGA26 

Storage 2.00 Tscf/yr ± 25% A.G.A. Gas Factsz 

Production·· Wright Kilien estimated that the average throughput per 

dehydrator in production is 2 MMscfd. This value has been validated by analysis of three 

data sources, as follows: 

• The Wyoming dehydrator survey (as adjusted by Wright Killen), which 

is believed to be the most comprehensive survey, reports a value of 2 

MMscfd per production segment dehydrator. 

• The TMOGA survey produced an average throughput value in the 8.1 

to 8.5 MMscfd range, but the study is believed to be biased toward the 

higher values in the production segment. This bias is caused by the 

higher production rate per weii in the Texas region. Therefore, the 

median value (as opposed to the average value) of the Texas survey is 

likely to be more representative of a true national average, since the 

median value is not heavily weighted by a few large dehydrators. The 

median value of the Texas (TMOGA) survey is 2 MMscfd. 

• The site visits produced an average of 12.5 MMscfd/dehydrator. 

However, many of the sites visited have higher production rate-tonumber of wells ratios than the naticnal average. If the two largest 

dehydrators, which are in the Gulf Coast, are excluded from the total, 

the resulting average is only 1.14 MMscfd/dehydrator. 

73 
The total production throughput was calculated by multiplying the average 

throughput per dehydrator by the number of dehydrators in the production segment and by a 

capacity utilization factor. Operators of several production sites studied for another GRI 

project indicated that the typical production dehydrator was using less than half its original 

capacity. The capacity utilization factor was determined to be 0.45 ± 33%. 

Gas Processing--Throughput for the gas processing industry segment was 

determined from the annual Oil and Gas Journal survey of processing plants.9 It was 

assumed that plants using a refrigerated process have glycol dehydration and plants using a 

cryogenic process have some type of dry-bed dehydration with no methane emissions. For a 

total gas processing throughput of 17.44 Tscf/year, these assumptions lead to a flow of 8.63 

Tscf/year that is dehydrated by glycol. No distinction was made between triethylene glycol 

(TEG) and ethylene glycol (EG), since the methane emissions for the two glycol processes 

are similar. [Note that benzene, toluene, ethyl benzene, and xylene (BTEX) emissions for 

EG may be several orders of magnitude lower than for TEG.] 

Transmission-Transmission segment throughput was determined in much the 

same way as was production. The Wright Killen survey indicated an average transmission 

dehydrator throughput of 14.8 MMscfd and an adjusted count of 201 transmission 

dehydrators, for a total throughput of l.09 Tscf/year. 

Storage--Storage segment throughput was determined from data in A. G.A. Gas 

Facts, Table 4-3. 2 Withdrawals from storage in 1992 were 2.41 Tscf. It was assumed that 

most gas taken from storage is dehydrated, so storage dehydrator throughput was estimated 

to be 2.00 Tscf/year ± 25%. 

74 
8.2.3 Dehydrator Flash Drums 

One of the dehydrator equipment accessories that can have a large impact on 

emissions is the three-phase flash drum. Data on flash drum existence were available from 

the TMOGA survey27 and from the site visits. The results are shown in Table 8-5. 

As discussed previously, the production segment of the TMOGA database is 

believed to be biased toward larger dehydrators, since the Texas production per well ratio is 

higher than the national average. Since analysis of the TMOGA database does show a 

relation between dehydrator size and the existence of a fla-;h drum {especially for sizes 

below 2 MMscfd), the number uf large dehydrators in the TMOGA database may bias the 

average f1ash drum percentage high. The site visits may be biased toward smaller 

dehydrators but are not believed to have the same degree of bias as the TMOGA data set. 

The result was a 3.31% number for production dehydrators. 

8.2.4 Dehydrator Gas Pumps 

Another important piece of glycol dehydrator equipment is the type of pump 

that circulates glycol. The activity factor for gas-driven glycol circulation pumps is 

calculated on the same basis as the dehydrator vented emissions (i.e., Tscf/year gas 

throughput for each industry segment). The total gas throughputs listed in Table 8-4 were 

multiplied by the fraction of dehydrators using gas-driven pumps to determine the gasdriven pump activity factor. This value is based on the average from Radian site surveys of 

the number of pumps per dehydrator. The results are displayed in the Table 8-6. 

8.2.5 Dehydrator Stripping Gas Use 

The percent of dehydrators using stripping gas was determined from the site 

surveys. The site surveys showed that less than 0.5% of all production dehydrators use 

stripping gas. The use of stripping gas is more common in the other three industry 

segments, with 11.1% for gas processing, 7.4% for transmission, and 8.0% for storage. 

75 
TABLE 8-5- U.S. GAS INDUSTRY DEHYDRATOR FLASH DRUMS 

Percent of Dehydrators with Flash Drwn~ 

(with 90% confidence bounds) 

Unadjusted 

TMOGA Site Selected 

Industry Segment Survey Results Visits Numbers Basis 

Production 

Gas 

42.4 ± 8.5% 3.31 ± 43.2% 26.5 ± 8.35% TMOGA and site visits 

Processing Plants 66.7 ± 8.5% 66.7 ± 46.5% 66.7 ± 10.1% TMOGA and site visits 

Transmission 66.9 ± 9.7% NA 66.9 ± 9.70% TMOGA 

Storage NA 52.0 ± 33.6% 52.0 ± 33.6 Site visits 

The number 

production, 

of dehydrators were divided. by survey: Unadjusted TMOGA results (618 in 

207 in processing, and 192 in transmission/storage) and site visits (408 in 

production, II in processing, 2 in transmission, and 25 in storage). 

TABLE 8-6. SITE SURVEY GAS PUMP DATA 

Active Gas Pumps per Total Industry Gas-driven Pump 

Dehydrator 

Segment 

Throughput Throughput 

(with 90% confidence limits) Tscf/year Tscf/year 

Production 0.891 ± 2.79% 12.4 ± 48.2% ILl ±62.0% 

Gas Processing Plants 0.111 ± 186% 8.63 ± 22.4% 0.958 ± 192% 

Transmission 0 1.09 ± 130% 0 

Storage 0 2.00 ± 25.0% 0 

Total 

24.1 ± 26.8% 12.1 + 58.6% 

8.2.6 Dehydrator Vapor Recovery Use 

Due to regulations restricting benzene, toluene, ethylbenzene, and xylene 

(BTEX) emissions, many sites have begun to install vapor recovery systems on glycol 

dehydrator reboiler/regenerator vents. However, not ali of these systems prevent the release 

of the methane since methane emissions are not regulated. Because of the lack of prevention 

against methane release, the only vapor recovery systems counted for this study were those 

that captured and burned the methane from the vent. The results are displayed in Table 8-7. 

The site visits did not identify any gas processing plants with vapor recovery systems that 

consume methane. However, there are likely to be some plants with methane vapor 

76 
recovery. For calculation purposes, the fraction of plants with methane emission controls 

was assumed to be 0.10 ± 90%. 

TABLE 8-7. SITE SURVEY STRIPPING GAS AND VAPOR RECOVERY DATA 

Segment 

Production 

Gas Processing Plants 

Transmission 

Storage 

Fraction of Dehydrators with 

Active Stripping Gas 

(with 90% confidence limits) 

0.0047 ± 116% 

0.111 ± 186% 

0.074 ± 118% 

0.080 + 118% 

Fraction of Dehydrators With 

Vapor Recovery that Consumes 

Methane 

(with 90% confidence limits) 

0.012 ± 73.1% 

0.0 ± 0% a 

0.148 ± 80.3% 

0.160 ± 80.0% 

• No vapor recovery was found during the site visits but it is Iikdy that some processing plants have 

flares. A value of 0.10 ± 90% was used in the calculations. 

77 
9.0 DISTRffiUTION SEGMENT 

Activity factors were developed for each of the sources of methane emissions 

from the distribution segment of ihe gas industry. The activity factors that were used in the 

distribution segment are the following: total leaks in underground distribution mains and 

services; metering and pressure regulating stations; customer meters; and total miles of mains 

and services used to detennine losses from pipeline third-party damage (i.e., dig-ins) as well 

as pipeline blow and purge for maintenance/installation activities. A discussion of the 

methodology used to derive these activity factors is presented in this section. 

9.1 Distribution Annual Equivalent Leaks 

Since the emission factor for quantifying emissions from underground 

distribution mains and services was stratified by pipe use (mains versus services) and by pipe 

material (i.e., cast iron, cathodically protected steel, unprotected steel, plastic, and copper), 

the activity factor was also stratified to extrapolate emissions. 

With the exception of cast iron main pipeline, the activity factor used to 

extrapolate the leakage estimate for underground disa:ibution mains and services was the 

number of annual equivalent leaks. (For cast iron pipeline, the activity factor was the total 

mileage of cast iron mains in the United States, which is a nationally tracked statistic.28) 

Annual equivalent leaks are defined as the number of leaks that leak continuously year round. 

For example, if leaks that are repaired during the year are leaking for half the year, on 

average, then each repaired leak would be counted as half an annual equivalent leak. 

The number of annual equivalent leaks was derived from the national database 

of leak repair records broken down by mains and services (U.S. Deparonent of 

Transportation, Research and Special Programs Administration).28 To allocate leak repairs 

into pipe material categories, data were collected from ten local distribution companies 

78 
representing different regions within North America. An estimate of the national leak repairs 

allocated by pipe material type is shown in Table 9-1. 

To derive arumal equivalent leaks from the national leak repair records, 

additional information was needed including the number of leaks found during the year (leak 

indications) and the unrepaired leaks at the beginning of the year (outstanding leaks). Since 

leak indications and outstanding leaks are not tracked nationally, this information was 

requested from individual companies. 

Data were collected from the companies participating in the cooperative leak 

measurement program on the annual number of leak repairs, number of leak indications, and 

outstanding leaks at the beginning of the year (reference year in most cases was 1991). The 

data were requested to be disaggregated by mains versus services and by pipe material. 

Complete data were provided by only four companies, coupled with a breakdown of the total 

mileage of mains and number of services by pipe material. Two additional companies 

provided data without a breakdown by pipe material or use. (The leak data disaggregated by 

pipe use and material type are difficult to obtain from many companies, since leak records 

are often not maintained in this manner.) 

An estimate of the total annual equivalent leaks for each of the six companies 

was developed for each pipe material category except cast iron, based on the following 

formula: 

where 

TEL 

OL 

TEL = OL + Ll + UDL + URL - (0.5 x RL) 

Total annual equivdent leaks; 

Outstanding leaks at the beginning of the year; 

79 
TABLE 9-1. NATIONAL LEAK REPAIRS ALLOCATED BY PIPE MATERIAL CATEGORY 

Estimated 

Average Leak National 

Pre<:ision 

Extrapolated Percent Leak Total Leak (Leak 

Pipe Use Pipe Material Repairs/Mile" Miles/Servicesb Leak Repairs Repairs Repairs 

Mains Cast 

Repairs) 

Iron 1.38 55,288 76,353 34 69,750 

Protected 

39,957 

Steel 0.08 451,466 34,943 16 31,922 

Unprotected 

14,006 

1.09 82,109 89,417 40 81,685 50,645 

Steel 

Plastic 0.08 299,421 25,151 II 22,976 22,862 

Subtotal 

255,864 100 206,33Jb 

Services Protected Steel 0.006 20,352,983 !26,188 42 181,752 

Unprotected 

206,405 

0.027 5,446,393 148,686 50 214,156 190,569 

Steel 

Plastic 0.001 17,681,238 22,986 8 33,107 

Copper 

26,238 

O.OII 233,246 2,519 I 3,628 3,165 

Subtotal 

300,379 100 432,643b 

Based on data provided by ten companies. 

b Based on nationally tracked database, U.S. Department of Transpottation, Research and Special Programs AdministrationP 
LI Leak indications recorded during the year, including call-ins; 

UDL Undetected leaks which cannot be found using an industry 

standard survey procedure; 

URL = 

RL 

Unreported leaks that have developed in parts of the network not 

survey during the current year; and 

Repaired leaks -- estimated to be leaking half the year, on 

average. 

Undetected leaks that cannot be found using an industry standard survey procedure were 

quantified based on information provided by Southern Cross. 29 According to their experience 

in perfonning leak surveys and survey audits, Southern Cross predicts that a standard 

industry survey procedure using a flame ionization detector (FID) instrument fmds 85% of 

the leaks. (Note: The standard industry survey procedure involves using either a walking or 

mobile survey, as appropriate for the area being surveyed, using an FID instrument. Any 

potential leak that is found with the FID instrument, registering any concentration above 

background, is investigated using bar holing procedures.) Therefore, the number of 

undetected leaks is estimated by: 

UDL = [(1/0.85)- 1] x U 

The total annual equivalent leaks are derived using the estimated leak duration for each type 

of leak, based on the following: 

• Repaired leaks are assumed to be leaking half the year, on average. 

• Outstanding leaks, leak indications, and undetected leaks are estimated 

to be leaking the entire year (i.e., 8760 hours per year). 

The leak duration of unreported leaks is factored into the estimation methodology for these 

leaks. Unreported leaks are those leaks that occur in parts of the network not surveyed 

during the year (i.e., multi-year survey cycle). The number .of unreported leaks is based on 

81 
the annual leak indications and the undetected leaks as well as the frequency of the leak 

survey. The number of unreported leaks in the system that is surveyed every n years is 

calculated based on the following: 

• For the first year in the cycle -- 1/n X (LI + UDL) leaks are leaking 

half the year; (n-l)in X (Ll + UDL) leaks are not yet leaking. 

• For the second year in the cycle -- lin x (LI + UDL) leaks are 

leaking the erttire year; lin X (Ll + UDL) leaks are leaking half the 

year; and (n-2)in X (Ll + UDL) leaks are not yet leaking. 

• For the third year in the cycle -- 2in X (Ll + UDL) leaks are leaking 

the entire year; lin X (Ll + UDL) leaks are leaking half the year; and 

(n-3)in x (Ll + UDL) leaks are not yet leaking. 

• For the fourth year in the cycle -- 3in X (Ll + UDL) leaks are leaking 

the entire year; lin X (Ll + UDL) leaks are leaking half the year; and 

(n-4)in X (Ll + UDL) leaks are not yet leaking. 

Based on the methodology described above, the number of equivalent leaks was determined 

for each of the six companies providing detailed data. The ratio of equivalent leaks to leak 

repairs was then calculated for each of the companies. The average ratio of equivalent leaks 

to leak repairs was used to extrapolate the national database of leak repairs. Table 9-2 

presents a summary of the leak record data provided by the six companies, the estimated 

equivalent leaks, and the corresponding ratio of equivalent leaks to leak repairs. As shown, 

the average ratio of equivalent leaks to leak repairs is 2.14. 

The national number of annual equivalent leaks, broken down by pipe use and 

material type. is shown in Table 9-3. As shown, the activity factor for cast iron mains is 

miles of pipeline, to correspond to the emission factor in units of scf/mile-year. The 

estimate of annual equivalent leaks is highest for unprotected steel services, followed by 

protected steel services. For mains, unprotected steel is the category with the highest 

estimated annual equivalent leaks. The precision of the number is based on the variability in 

leak repair data allocated by material type from ten companies and the variability in the ratio 

of equivalent leaks per leak repair from six companies. 

82 
co 

w 

TABLE 9-2. SUMMARY OF LEAK RECORD DATA FROM PARTICIPATING COMPANIES 

:': ~\:n"iuJlif Le3':o- ' ',•, 

,',.,' / 

...... .. 

.. 

COmpllliy 

_Aftilual_. Outst.aDdihj; ·· ' Estltnated Tof.ll / •' Ratio. or l;qnlv8ient-I,.eaiks 

·' · ·i'ridkationS ·.·. Ann""! L<ak Re~i$ Leaks. ... - EqUiYal~t· ·uaM:; .. :::::: .-.:::::_ -:-.to· Littk·~~itepaits/:·· 

A 3,747' 2,061" 0 3,378 1.64 

B 9,249 17,003 ll,701 18,796 !.II 

c 2,115 2,443 0 2,832 1.16 

D _ .. 14,681 .. • 41,286 2.81 

E 1,999 2,287 2,396 6,250 2.73 

F 5,992 3,421 1,558 11,597 3.39 

Average 

2.14 

Precision 

0.79 

Mains only. 

b Data not available. 
TABLE 9-3- SUMMARY OF ACTIVITY FACTORS FOR DISTRIBUTION 

UNDERGROUND PIPELINES 

Average Activity Precision of Activity 

Estimated Total Factor Factor 

Pipe Use Material 

(equivalent 

Category Leak Repairs" (equivalent leaks)b lea!o;) 

Mains Cast Iron 69,750 55,288' 2, 764" 

Protected Steel 31,922 68,308 42,545 

Unprotected Steel 81,685 174,657 101,685 

Plastic 22,976 49,226 58,0!8 

Subtotal 206,333 

Services Protected Steel 181,752 390,628 526,354 

Unprotected Steel 214,!56 458,476 499,850 

Plastic 33,107 68,903 66,840 

Copper 3,628 7,720 8,521 

Subtotal 432,643 

• Based on national leak repair database27 am! data provided by six companies (see Tables 9-l and 9-2). 

h Based on ratio of annual equivalent leaks to leak repairs of 2.14 (see Table 9-2). 

' Miles. 

84 
9.2 Distribution Metering and Pressure Re¥1Jiating Stations 

Since the emission factor for predicting emissions from metering and pressure 

regulating stations was stratified into station type, inlet pressure range, and location 

categories, the activity factor was also stratified to produce an overall estimate of emissions 

from stations in each segment of the gas industry. 

For distribution stations, a questionnaire was sent to distribution companies 

participating in the underground leak measurement program and the companies participating 

in the metering/pressure regulating station measurement program. A total of ten companies 

provided demographic information on the stations in their distribution network. The station 

counts provided by the companies were disaggregated by station type and inlet pressure 

range. Table 9-4 presents the demographic information provided by the respective 

companies. 

To extrapolate the individual company data to a national number of stations, a 

known industry statistic was used. The individual company demographic data were 

combined with the miles of main and total gas throughput for each company to detennine the 

number of stations in each category on a per mile of main and per unit of throughput basis. 

Linear regression analyses were performed on the number of stations versus both miles of 

mains and total gas throughput. respectively. It was concluded that the number of stations 

should be extrapolated by miles of mains since the variability within the sample set was 

lower for number of stations per mile than for number of stations per throughput. 

The average number of stations per mile was calculated from the individual 

company data and multiplied by the total mileage of main pipeline in the United States.28 

The standard deviation of each number was calculated based on the variability in the 

individual company data. 

85 
TABLE 9-4. ACTIVITY DATA PROVIDED BY INDIVIDUAL COMPANIES 

Station Type 

Inlet 

Range 

Pressure Comp Comp Comp. Comp. Comp. Comp. Comp. Comp. Comp. Comp. Comp. Cornp 

(psig) A B c D E F G H I J K L" 

M&R Stations 

>300 14 6 18 19 .. 

" I 0 .. 29 128 15 

100-300 8 15 0 0 .. 600 0 0 .. 286 431 2 

40-100 I 2 0 0 .. 0 0 0 .. 170 m 0 

<40 0 0 0 0 .. 0 0 0 .. 48 97 0 

Regulating 

Stations 

>300 720 0 0 0 0 30 2 I 34 29 87 0 

100-300 1,187 0 258 94 

" 210 0 0 44 286 

"' 273 

40-100 207 1,257 0 325 44 400 II 8 136 170 2,122 110 

<40 12 0 130 28 203 1.000 0 0 53 48 935 0 

Miles Main 40,930 30,934 7.594 4.000 3,924 14,900 64 78 4.109 3,396 29,073 1,263 

• Not considered representative of national average. 
Data were collected from three of the ten companies on the number of 

regulating stations in vaults versus above ground. Based on the data collected, about half of 

the regulating stations with an inlet pressure greater than 300 psig were observed to be 

located in vaults. For regulating stations with an inlet pressure between 40 and 300 psig, the 

majority of stations in urban areas were in vaults and the majority in rural areas were above 

ground. On average, it was estimated that 55% of the stations are located in vaults with an 

inlet pressure between 100 and 300 psig. 

For regulating stations with an inlet pressure between 40 and 100 psig, about 

70% of the stations are located in vaults. Based on the data collected, essentially all lowpressure ( < 40 psig) stations are located in vaults. Table 9-5 shows the number of stations 

disagg~egated by station type, inlet pressure range, and location in a vault versus above 

ground. 

9.3 Customer Meters 

The total number of customer meters in the U.S. gas industry, 56,132,300, 

and the number of residential customer meters, 51,524,600, were based on Gas Facts, Table 

8-1. 2 The number of residential customer meters located indoors versus outdoors was 

estimated based on a regional breakdown of total customers presented in Gas Facts. Based 

on data collected from 22 companies located in six regions within the United States, the 

majority of customer meters in urban, northern areas of the country are located indoors, 

while the majority of customer meters located in non-urban areas and in the southern areas 

are located outdoors. On average, approximately 22% of residential customer meters are 

located indoors. The leakage rates from customer meters located indoors was assumed to be 

negligible based on the increased probability that leaks on indoor meter sets are detected and 

repaired promptly. This assumption of negligible leakage from indoor meters is consistent 

with the findings from pressure regulating stations located in vaults. 

87 
00 

00 

TABLE 9-5. SUMMARY OF ACTIVITY FACTORS FOR DISTRIBUTION METERING/PRESSURE 

REGULATING STATIONS 

M&R >300 A·G 0.004 3,460 2,458 

M&R 100-300 A-G 0.016 13,335 14,091 

M&R < 100 A-G 0.009 7,127 10,374 

Reg. >300 A-G 0.005 3,995 2,702 

Reg. >300 Vau!l 0.003 2,346 1,587 

Reg. 100-300 A-G 0.015 12,273 7,461 

Reg. 100-300 Vault 0.007 5,514 3,352 

Reg. 40-100 A-G 0.043 36,328 23,375 

Rog. 40-100 Vault 0.039 32,215 20,729 

Reg. <40 0.018 15,377 9,922 

• The above-ground and in-vault categories were combined for the low pressure regulating station category. 
Therefore, the number of outdoor residential customer meters, 40,049,300, 

was 78% of the total population of residential customer meters. The confidence interval 

around the mean activity factor, ± 10%, was estimated based on the variability in company 

supplied data. 

The precision of the total commercial/industrial customer meters is assumed to 

be ± 5% of the estimated 4,608,000 meters. 

9.4 Distribution Mains and Services 

The total mileage· of distribution mains and services was used as the activity 

factor to estimate the methane emissions from distribution third-party damage (e.g. dig-ins) 

and from blow and purge due to pipeline abandonment, insta1Iation, and repair. The total 

miles of main pipeline in the gas industry, 836,700 miles, was based on U.S. Department of 

Transportation (DOT) records." The total miles of service, 460,809 miles, was reported by 

A.G.A. 2 Therefore, the total combined main and service mileage in the United States is 

1,297,569 miles. The 90% confidence limit around the activity factor is assumed to be 5%, 

or 64,879 miles. 

89 
10.0 RESULTS 

This section summarizes the major activity factors collected during this 

project. Some activity factors were already known on a national basis and were taken from 

published sources. Others were generated from company data or site visit data. Most 

equipment counts taken at site visits excluded equipment that was not in use. The following 

tables (10-1 and 10-2) summarize the activity factor data. Previous sections of this report 

explain the detailed basis for all of the activity factors. 

TABLE 10-1. WELL-DEFINED ACTIVITY FACTORS 

Segment Activity Factor Name Number 

Total Industry 1992 Gross Gas Production (Tscf) 22.13 

!992 Marketed Gas Production (Tscf) 18.71 

Production No. of Gas Wells 276,000 

No. of Oil Wells 602200 

Processing No. of Gas Plants 726 

Transmission and Storage Miles of Trans Pipeline 284,500 

No. of Storage Facilities 475 

No. of Storage Wells 18,000 

Distribution Miles of Service 460,800 

Miles of Mains 836,760 

Number of Residential Customer Meters 51,520,000 

90 
TABLE 10-2. EXAMPLES OF DEVELOPED ACTIVITY FACTORS 

Segment ActivitY Factor Name Number 

Total Industry Reclp Compressor Drivers 44,130 

Turbine Compressor Drivers !,543 

Number of Glycol Dehydrators 39,620 

Production No. of Oil Wells Marketing Gas 209,000 

No. of Gas Wells Requiring Unloading I !4,!00 

Compressor Drivers 17,!00 recips 

Engine MMHp-hr 27,460 

Offshore Platfonns I, I 15 

Dehydrators (% of industry dehydrators) 37,820 (95%) 

Separators 167,200 

In-line Heaters 51,000 

Total Production Vessels 256,000 

Chemical Injection Pumps 16,970 

Pressure Relief Valves 529,400 

Gathering Pipeline Miles 340.200 

Well Workovers 9,329 

Pneumatic Devices 249,100 

Processing Compressor Drivers 4,092 recips 

726 turbines 

Annual Compressor Operation 27,780 MMhp·hr 

32,910 MMhp·hr 

Dehydrators (% of industry dehydrators) 498 (1.3%) 

Acid Gas Recovery Units 371 

Continued 

91 
TABLE 10-2. (Continued) 

Segment Activity Factor Name Number 

Transmission and Compressor Drivers 

Storage 

7,715 recips 

817 turbines 

Annual Driver Operating Hours (average) 

• Transmission Compressor Drivers 40,380 MMhp·hr 

9,635 MMhp·hr 

- Storage Compressor Drivers 4,922 MMhp·hr 

1,729 MMhp·hr 

Transmission Compressor Stations 1,700 

Dehydrators (% of industry dehydrators) 1,293 (3.3%) 

Pneumatic Devices 2,175 

M&R Stations 

- Fann Taps 71,690 

- Interconnects 2,533 

- Direct Industrial Sales 938 

Distribution M&R stations 132,000 

Outdoor customer meters . 40,049,000 

92 
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EPA-600/R-96-0801. 

and 

Gas Research Institute and U.S. Environmental 

Protection Agency, June 1996. 

Biederman, N. GR! TRANSDAT Database: Compressor Module. (prepared 

Chicago, 

for Gas Research Institute) npb Associates with Tom Joyce and Associates, 

IL, August 1991. 

94 
27. 

28. 

29. 

Texas Mid-Continent Oil and Gas Association (TMOGA) and Gas Processors 

Association (GPA), Dehydrator Survey Database, 1991. 

U.S. Department of Transportation, Research and Special Programs 

Administration, 1991. 

Southern Cross Corporation. "Comments on Docket PS-123 Notice I, Leakage Surveys," 49 CFR Part 192, Department of Transportation, Research and 

Special Programs Administration, Materials Transportation Bureau, Office of 

Pipeline Safety Regulations, December 19, 1991. 

95 

APPENDIX A 

Site Data 

A-1 
TABLE A-1. PRODUCTION SITES (Summary) 

. : . ,: . .. -- ',• 

f GC •... 

'::.-Total-Us .. :·.' ... :.-.j-:Region'O\':·::·..... .-:· _ _. .. .- :9tts·riore CP PM •AGL,.· "' ' " 

Site 

6 9 7 9 19 50 sites** 

Companies 4 7 7 4 10 32 companies 

Survey Type 

- Site Visit I 9 3 9 2 24 site visits 

- Phone Survey 5 0 4 0 5 14 phone surveys 

- Star Sile Visit•• 0 0 0 0 12 12 star sites•• 

**This 

•Region 

does 

Key: 

not 

GC 

include 

= Gulf 

ail 

Coast; CP = Central Plains; PM = Pacific Mountain; AGL = Atlantic & Great Lakes. 

sites visited·by Star or other fugitive emission contractors. Only the sites used for activity factor data collection are 

included. 
TABLF. A-2. PRODUCTION SITF.S (Offshore Uata) 

Region GC-Oft GC-Off GC-Off GC-Off PM-Off PM..Qff 

Site I 2 3 4 5 

' Compariy I 2 2 2 3 4 

Survey Type v p p p p p 

Gas Marketed (MMcfd) 0.365 12.5 440 4 17.5 160 

Gas Produced (MMcfd) 05 12.5 440 4 17.5 160 

Equipment Counts: 

Gas Wells 2 0 80 0 0 12 

Oil Wells 3 150 0 40 22 0 

+Oil wells that market gas 3 150 0 40 22 0 

Separntors 4 0 24 0 0 1 

In-line Heaters 0 0 0 0 2 

Pneumatic Devices 3 0 32 0 0 0 

Chern lnj Pumps 0 0 0 0 0 0 

Comp~ssors• 0 - - - 2 

Dehydrntors 1 0 8 1 2 3 

+ Dehy witb 3 ph Flash 1 

+Dehy witb Vent Control 0 

+ Dehy w/Kimray ~mps 1 

+ Dehy w/Stripping Gas 0 

Miles of Gatbering Pipeline - - - - - Fugitive Component count y N N N N N 

Vented (Site Blow & Purge Data) y y y y y y 

Notes: 1) Survey Type V = Site Visit (Radian); P = Phone Survey: S = Sue Visit (Star). 

l) * = Gas lift cumpre~~urs not included. 

J) Y =Yes. N =No;·-·"' No Data. 

4) Region Key: GC = Gulf Coast PM = Pacific Mountain. 

Total Equiprnenll 

OfT short! Totals Total Wells 

& Sitts 

4 Companies GC PM GC PM 

456.9 117.5 1.66 5.22 

4.57.0 177.5 1.66 5.22 

82 12 

193 22 

193 22 

28 1 0.10 O.o3 

0 2 0.00 0.06 

35 0 0.13 0.00 

0.00 0.00 

0 2 0.00 0.17 

10 

' 0.04 0.15 

1 0 

0 0 

1 0 

0 0 

Equipment/ Equip./Mkt. Gas Equip.Prod. Gas 

Gas Wells (1/MMcfd) (JJJ\.1Mdd) 

GC PM GC PM GC PM 

0.34 0.08 006 O.ot 0.06 0.01 

0.00 0.00 0.00 O.QJ 0.00 O.GI 

0.43 0.00 0.08 0.00 0.08 0.00 

0.00 0.00 0.00 000 0.00 0.00 

0.00 0.17 0.00 0.01 0.00 O.Dl 

0.11 0.25 0.02 0.03 O.D2 0.03 
TABU: A-3. PRODUCTION SITES (Gulf Coast On~hore nata) 

Region GC GC GC GC GC GC GC GC GC Total GC 

Site 7 8 • 10 11 12 13 14 15 

Equip./ Equip./ 

Company 5 • 7 8 9 10 11 11 11 9 Sites Equip./ Equip./ Mkt. Gas Prod. Gas 

Survey Type v v v v v v v v v 7 Companies Total Total Wells Gas Wells (1/MMcfd) 

Gas Marketed 

(1/Mi\fcfd) 

(MMcfd) 23.1 25_5 124 54 28 250 1.9 7 130 643.4 0.54 

Gas Produced (MMcfd) 23.1 2S.S 124 

" 28 250 1.9 7 130 643.4 0.54 

Equipment Counts: 

Gas Wells 

Oil Wells 

13 80 18 130 26 300 0 10 31 608 

50 0 

' ' 0 300 

"' 127 0 638 

+Oil wells !hat market gas so 0 3 3 0 300 155 68 0 

Separators 

579 

" 80 42 71 26 300 0 II 

" , .. 0.50 0.99 0.93 0.93 

> In-line Heaters 2 

" 17 23 26 0 0 12 0 136 0.11 0.22 0.21 0.21 

• 

... Pneurn:ldc Devices 68 170 0 68 109 22S 0 II 

" 682 0.:;7 1.12 1.06 1.06 

Chern lnj Pumps 10 

' 0 ' 0 0 0 0 0 20 0.02 O.o3 O.oJ 0.03 

Compressors* 12 4 2 37 0 0 0 IS 10 80 0.07 0.13 0.12 0.12 

Dehydrators 7 2 2 12 26 2 0 4 26 81 O.o7 0.13 0.13 0.13 

+ Dehy with 3 ph Flash 0 0 2 ·2 0 0 0 4 0 8 

+Dehy with Yen! Control 4 0 2 0 0 0 0 0 0 

+ Dehy w/Kimray Fumps 7 

6 

I I 6 26 2 0 0 26 

+ Dehy w/Srripping Gas 0 

69 

0 0 0 0 0 0 0 0 

Miles of Gathering Pipeline - 46.3 

0 

Fugitive Component count 

26.4 40 8 - - - y y y y y y 

\',~nted {Site Blow & Purge Data) y y y 

- - - y y y y y y 

Notes: I) Survey Type V = Sire Visit (Radian): P = Phone Survey; S =Site Visit {S!ar). 

2) • = Gas lift compressors not included. 

3) Y =Yes, N =No; •.· = NoDara. 

4) Region Key: GC = Gulf Coast. 
-I AHI .F A-4. PRODUCTION SITES (Central Plains Data) 

Rt>gion CP CP CP CP CP CP 

Site 16 17 18 19 

" 21 

Company IZ 13 14 IS 16 17 

Survey Type v v v p p p 

Gas Marketed {MMcfd) 42.7 180 196 7 0.2 19.8 

Gas Produced {MMcfd) 42.7 180 1% 7 0.2 20 

Equipment Counts: 

Gas Wells 138 321 !000 400 I 100 

Oil Wells 55 II 0 0 0 0 

+Oil wells that market gas 55 II 0 0 0 0 

Separators 130 321 7 400 I 100 

In-line Heaters 138 321 0 400 0 0 

Pneumatic Devices 449 963 667 100 

Chem lnj Pumps 28 273 0 13 0 0 

Compressors• 31 50 64 

Dehydrators 16 220 0 400 0 25 

+ Dehy with 3 ph Flash 0 0 0 0 

+Deity with Vent Control 0 0 0 . 

+ Dchy w/Kimray Pumps 16 220 0 25 

+ Dehy w/Stripping Gas 0 0 0 

Miles of Gathering Pipeline 5.2 . 600 . - . 

Fugitive Component count y y y . - . 

Vented (Site Blow & Purge Data) y y y 

Notes: I) Survey Type V = Site Visit {Radian); P = Phone Survey; S = Site Visit {Star). 

2) * = Gas lift compressors not included . 

.l) Y '" Yes. N = N(>; -.- =No 0dta. 

4) Region Key: CP = Central Plams. 

CP Total CP 

" 

18 7 Sites Equip.! 

p 7 Companies Total Total Wells 

2 447.7 0.22 

2 I 448.0 0.22 

" 1975 

4 70 

4 70 

I 960 0.47 

0 859 0.42 

0 2179 1.33 

0 314 0.15 

I 146 0.09 

I 662 0.32 

. 0 . 0 

0 261 . 0 

. 

. 

Equ.ip.f Equip./ 

Equip.! Mkt, Gas Prod. Gas 

Gas Wells (lfMMcfd) {lfMMcfd) 

0.49 2. !4 2. !4 

0.43 1.92 1.92 

1.38 4.95 4.94 

0.16 0.70 0.70 

0.10 (}.35 0.35 

0.34 1.48 1.48 
)> 

• 

"' 

TARLE A-5. PRODUCTION SITES (Pacific/MotJntain Onshore Data) 

Region PM PM PM PM PM PM PM 

Site 

" 24 25 

" 21 28 2? 

Company 19 20 21 21 21 21 22 

Suney Type v v v v v v v 

Gas Markered (MMcfd) 4 104 0.138 0.03 O.o2 0.035 0.8 

Gas Produced (MMcfd) 4 307 0.138 0.03 0.02 0.035 0.8 

Equipment Counts: 

Gas Wells S3 0 0 0 0 0 0 

Oil Wells 0 913 18 8 10 

" 20 

+Oil wells that market gas 0 137 18 8 !0 

" 20 

Separato~ 45 0 0 0 0 0 0 

In-line Heate~ S3 5 3 2 0 0 0 

Pneumatic Devices 80 0 0 0 0 0 0 

Chern fnj Pumps 36 0 0 0 0 0 0 

Comprcsso~• 17 IY 0 0 0 0 I 

Dehydrators 5 0 0 0 0 0 I 

+ Dehy with 3 ph Flash 0 0 0 

+ Dehy with Vent Control 0 .o 0 

+ Dehy w/Kimr.ty Pumps 5 0 I 

+ Dehy wfStripping Gas 0 0 0 

Miles of Gathering Pipeline . . . . . . 

Fugitive Component count y y N N N N N 

Vented (Site Blow & Purge Data) y y N N N N N 

Notes: I) Survey Type V = Site Visit (Radian); P = Phone Survey; S = Site Visit (Star). 

2) • = Gas lift compressors not included. 

Jl Y = Yes. N =No:·.· =No Data. 

4) Region Key- PM = Pacific Mountam. 

PM PM Total PM 

30 31 

21 21 9 Sftf:! Eqllip.l 

v v 4 CompanJes Total Total Wells 

0.1 11.082 120.2 0.12 

0.1 11.082 323.2 0.32 

0 0 53 

7 728 1719 

7 728 943 

0 45 0.17 

0 6J 0.24 

0 0 80 0.08 

0 0 36 O.Q4 

I 38 0.14 

0 6 0.02 

0 

0 

6 

0 

. . 

N N 

N N 

Equip./ Equip./ 

Equip./ Mkt. Gas Prod. Gas 

Gas Wells (t/MMcfd) (l!MMcfd) 

0.85 0.41 0.14 

1.00 0.58 0.20 

1.51 0.67 0.25 

0.68 0.30 0.11 

0.32 0.35 0.12 

0.09 0.05 O.Q2 
TABLE A-6. PRODUCTION SITES (Atlantk & Great Lakes Data) 

Region AGL AGL AGL AGL AGL AGL AGL AGL AGL 

SUe J2 JJ 34 35 36 37 

" Company 

39 40 

" Survey Type 

24 25 26 27 28 29 30 JO 

v p p v p p F s s 

Gas Marketed (MMcfd) 24 6 15 17 12 16 81 O_!g O.IS 

Gas Throughput (MMcfd) 24 6 IS 17 12 20 81 0.19 0.19 

Oil Throughput (1000 BID) 0 

Equipment Counts: 

Gas Wel!s 

Oi! Wells 

800 250 1000 520 450 1582 4034 II II 

+Oil wells 

0 0 0 0 163 418 0 0 0 

that market gas 

Sepanttors 

0 0 0 163 418 0 0 0 

In-line Heaters 

lSI 250 500 520 4SO 1582 3227 - 2 

0 

Pneumatic Devices 

0 

76 0 10 520 450 1582 1294 

Chern lnj Pumps 

- 0 0 0 12 0 8 

" y 

Compressors• 

0 

Dehydnttors 

I - - - - - - 0 

0 2 I 30 0 0 ., + Dehy with 3 ph Flash 0 0 0 0 

' +Dehy with Vent C:mtrol 0 ' 2 

.+Dehy w/K.imnty Pumps 0 2 I 30 

+Dehy w/Stripping Gas 

8 0 

0 

Miles of Gathering 

21 0 

Pipeline - - - - Fugitive Componenl 

- -

Vented 

count y N N N y y y y 

(Site Blow & Purge Data) - - y y -

--

Notes: I) Survey Type V = Site Visit {Radian); P = Phone Survey; S = Site Visit (Star). (Continued) 

2) • = Gas lift compressors not included. 

3) Y =Yes, N =No;·-· =No Data. 

4) Region Key: AGL = Atlantic & Great Lakes. 
> 

00 ' 

TABLE A-6. (Continued) 

Region AGL AGL AGL AGL AGL AGL AGL AGL AGL AGL TlllaiAGL 

Site 

Company 

41 42 43 44 45 46 47 .. .. 51) 

30 31 31 31 31 31 31 31 

" Suney Type 

31 19 Sites 

s s s s s s s s s s 10 Companies 

Gas Marketed (MMcfd) 0.17 0.39 0.37 0.37 0.18 0.23 0.30 0.35 0.13 0.35 

Gas Throughput (MMcfd) 0.17 0.39 0.37 0.37 0.19 0.23 0.30 0.35 0.13 0.35 

Oil Throughput(IOOO BID) 

Equipment Counts: 

Gas Wells 

Oil Wells 

10 23 22 22 II 14 18 21 8 21 

• • • • • +Oil wells 

0 0 0 0 0 

Sepanuors 

that market gas 0 0 0 0 0 0 0 0 0 0 

0 10 7 8 5 3 

" 17 

' In-line Heaters 

7 

Pneumatic Devices 

0 0 0 0 I I 0 0 0 0 

- -

Chern Inj Pumps 

- - - - - - - -

Compressor:s* 

0 0 0 0 0 0 0 0 0 0 

0 0 0 0 0 • Dehydrators 

0 0 0 0 

+ Dehy with 3 ph Aash 

- - - - - - - - - -

+Dehy with Vent Control 

+ Deby w/JGmrny Pumps 

+ Dehy w/Stripping Gas 

Miles of Gathering Pipeline - - - - - - - - - -

v~nted 

Furirive Component count y y y y y y y y y y 

(Site Blow & Purge Data) - - - - - -

Notes: 1 J Survey Type V "' Site Visit (Radian); P == Phone Survey; S "' Site Visit (Star). 

2) • == Gas lift compressors not included. 

3) Y == Yes, N =No;"-" = No Data. 

4) Region Key: AGL "' Atlanlic & Great Lakes. 

Equip.( Equip.! 

Total Equip./ Equip.! Mkt. Gas Prod. Gas 

(SiiM 32-50) Total Wells Gas Wdls (liMM:cfd) (I!MMcfd) 

173.6 

178.0 

8828 

581 

581 

6766 0.72 0.77 38.97 38.01 

2 0.00 0.00 0.07 0.07 

3932 0.43 0.4o 13.07 22.50 

45 0.00 0.01 0.26 0.25 

I 0.00 0.00 0.04 0.04 

74 0.0! 0.01 0.43 0.42 

5 

5 

41 

21 
TABLE A-7. GAS PROCESSING PLANTS 

' .. ······ 

.. ,'',' i ... i ,··· .. ·.·.·· .. ; >: 

Si .. ,i 1. '··,',·.·,· ·:· 

... I . ,.,,, .. "" ... .····3 

.. 4 ,, 

.. " '· 6 I> 7 8 9 

Companies I I 2 3 3 4 5 5 5 

Type Ccyo Ccyo Ccyo Lean Oil Ccyo Ccyo Lean Oil Refrig. Refrig. 

Abs., Abs. 

Ccyo 

Capacity (MMscfd) 100 75 70 850 900 40 130 130 

Current Throughput 49 60 56 350 750 140 40 130 130 

(MMscfd) 

Compr. Units 7 4 6" 9 I o••• 4.4** J.4U !.4** 

- Turb. Eng 0 0 0 2 I I 0 0 0 

- Recip. Eng 7 4 6" 7 0 0 4.4*. 1.4** 1.4** 

-Total HP 11000 3700 6740** 43300 27000 20000 5925** 6267** 6267** 

Dehys 0 I 2 3 0 0 I I I 

Dehys w/Kimray I 0 0 0 0 0 

Pumps 

Pneum Ongas 2 3 0 25 25 17 0 0 0 

Vented Data 

- Site y y y y - y 

-Company - - - - - y - - - Fugitive CC 639 357 799 !458 - - 6831 5902 5902 

• Count only of compressor blowdown open-ended lines, site open-ended lines, and compressor pressure relief valves. 

** Gas lift compressors not counted in the totals for this site with gas lift for oil recovery. 

*** I turbine drives 2 propane compressors. No natural gas compressors. 

"Y" ""Yes 

. TOtal10 Ji 'it Sites 

6 7 7 

companies 

Rcfrig./ 

Lean Oil 

Abs_ 

140 

70 

20 19 72 

5 2 10 

15 17 62 

59600 189799 

I 10 

I 

0 72 

y y 

- -

- Y' 
TABLE A-8. TRANSMISSION COMPRESSOR STATIONS 

.. ·.::•' .::·.Site NUm"he:~ ·,.::::·"· l .. ·.• ·. ·•··· ... /'{.' .. .... ..·. ' ' : 'j .·· . 4 5 9·· .. · ... .·· I& 'IIi' '6 .... 7 . .. .. 8 .. 

Company Number I I I 2 2 2 3 3 4 4 4 

Compr. Units 13 2 2 6 7 13 12 13 2 10 6 

- Turb. Eng 0 2 2 0 0 0 2 I 2  3 2 

- Recip. Eng 13 0 0 6 7 13 10 12 0 7 4** * 

-Total HP 32650 6900 6900 16900 10400 24800 14560 17570 40000 - -

Dehydrators 0 0 0 0 I - 0 0 6 0 0 

- Flash Tanks . I 6 

- Kimray Pumps 0 6 

- Stripping Gas 0 0 

-Vapor Recovery 0 0 

- Vent Flash Gas 

Pneum 

Wells 

48 12 - 8 - 20 75 40 68 83 50 

Not Applicable 

Fugitive CC 741** 223 .. 165** - - - 3038 3949 1730 1467 956 

Site B.D Practice~ y y - y y - - - y y y 

Co. BID No's - - - y y y - - - - -

(Continued) 
)> 

' 

,•', F.·•·•Sitt. Number ·"""'"(""" I ,;; il ),;>"! T. 13'" 

Company Number 5 6 

Compr. Units 18 2 

- Turb. Eng 0 2 

- Recip. Eng 18 0 

-Total HP 21000 - Dehydrators I 0 

- Flash Tanks I 

- Kimray Pumps 0 

- Stripping Gas 0 

- Vapor Recovery 0 

- Vent Flash Gas 

Pneum 3 

Wells 

Fugitive CC 1123 134 

Site R. D Practices y -

Co. BID No'~ - -

Fug cc does not include connections or tubing 

* "" Elec driven compressors 

** = Not including hydraulic valves 

*** = Recip Engine w/Centrifugal Compressor 

TABLE A-8. (Continued) 

·>!( .. ' ·.·•·•·•>.:Is· ·>16 .···· 17 .·. 

6 7 8 8 

2 26 5 3 

2 I I 0 

0 25 4 3 

- - - -

0 0 0 0 

Not Applicable 

284 1706 345 12 

- y - -

- - - -

"'*** = Does not include third turbine that was pennanently out-of-service 

"-" means no data available, "Y" = Yes 

J8 ··lit ~0 2l ~I Sit« 

9 9 10 10 10 Co's 

7 13 7 2 171 

0 3 0 2 25 

6 10 7 0 145 

- - - - 191.680 

0 0 - - 8 

38 - 0 

508 792 - -

y - y y 

- - - -
> 

' 

N 

TABLE A 9 - . STORAGE COMPRESSOR STATIONS 

........ ·····. ·.· .. ·.··· . . ., .... 'sn···.•.·.·.•·.·.·.·.·.··········.•.······· "' ... . ...... ' ···.··•• ; ...•.•..••... ·. 

Companies I 

Type UG 

Compr. Units 3 

- Turb. Eng 0 

- Recip. Eng 3 

-Total HP 6250 

Dehydrators 4 

- Flash Tanks 

- Kimray Pumps 

- Stripping Gas 

- Vapor Recovery 

- Vent Flash Gas . 

Pneumatics 18 

Wells ? 

Fugitive CC 1750 

Vented Data: 

-Site - -Company -

Fug cc does not include connections or tubing 

• = Elec driven compressors 

** = Not including hydraulic valves 

*** ""Recip Engine w/Centrifugal Compressor 

"-"means no data available, "Y" =Yes 

UG = Underground Storage Station 

> i.·····.·.······· 

2 

UG 

2 

0 

2 

2200 

I 

-

50 

-

y 

y 

LNG = Above ground, Liquefied Natural Gas Station 

.,• ~··· I•' . ·~· ... ·.·· ···.· 5 •. 6 

3 4 5 6 

UG UG LNG UG 

2 4 5' 18 

0 2' 0 4 

2 2 0 14 

9400 7000 10300* 48510 

I 8 0 I 

I 8 I 

0 0 0 

0 0 I 

0 0 

I 0 0 

68 127 4 -

22 83 0 64 

1113 8326 1679 887 

y y y y 

- y - -

··.· 7 8 

-•:,• -... -:·::::'_'_'_fjtot.SI'c --" •'•_ 

·•···· ... 8Sites. 

7 7 7 Co's 

UG UG 

9 9 52 

0 0 6 

9 9 41 

9000 11600 !04260 

- -

- - 217 

- - 219 

- - 13700 

y y 

- -
APPENDIX B 

Methods for Estimating Activity Factors 

B-1 
B.l 

ACTIVITY 

RATIO (SITE-WEIGHTED) METHOD FOR ESTIMATION OF AN 

FACTOR 

As discussed in Section 3.4, the ratio (site-weighted) method has been used to 

estimate activity factors on the basis of well counts or production. In that section, a 

nwnerica1 example is given which illustrates the ratio method for that purpose. This 

appendix provides a further description of the ratio method, including calculation of a 

confidence interval for an estimate obtained by this method. In Section B.1.1, estimation 

using the ratio method is described. In Section B. I .2. methods for computing a confidence 

interval for the estimate produced by the ratio method are discussed. Further discussion of 

the ratio method and methods for computing confidence intervals is provided by Cochran4 

(1977). 

B.l.l Estimation Using the Ratio Method 

Suppose 

y, 

x, 

n 

X 

N 

device count (e.g., number of separators) at the ith sampled site; 

value of the extrapolation parameter (number ot wells or gas 

production) at the ith site; 

number of sites sampled; 

the regional value of the extrapolation parameter, e.g. the total 

number of wells in the region; and 

the total number of sites in the region. 

For the purposes of illustration, this section discusses the estimation of the 

regional number of separators by using the well method. Then, by the ratio method, the 

following is the estimate of the number of separators per well: 

or 

B-2 
" 

This estimated number of separators per well and the regional number of wells is then used 

to estimate the number of separators in the region: 

B.1.2 Confidence Interval for Estimate Produced by the Ratio Method 

Cochran presents two approaches for estimation on the basis of confidence 

intervals. One method for calculation of the confidence interval is based on the assumption 

that the ratio estimate, R, is approximately normally distributed. In many applications, the 

normality assumption is satisfied only if the sample size (the number of sites visited in our 

application) is sufficiently large (at least 30) and the relative uncertainties (coefficients of 

variation) in both the average number of separators per site and the average number of 

wells per site are both sufficiently small (less than 10%). The suggested rules of thumb are 

given by Codrran. If the ratio is normally distributed, its confidence interval will be 

symmetric. 

If the ratio itself is not approximately normally distributed, but the numerator 

and denominator are both normally distributed, the ratio will tend to have an asymmetric 

confidence interval in which the upper confidence limit is more separated from the mean 

than is the lower confidence limit (see Figure B-1). A second method handles this case. 

As is discussed below, the cause of the asymmetry in some applications is a fundamental 

consideration in the selection of a method. 

8-3 
90% 

Confidence 

Limit 

mean 

X 

Emissions 

90% 

Confidence 

Limit 

a) Normal Distribution 

90% 

Confidence 

Limit mean 

x 

Emissions 

90% 

Confidence 

Limit 

b) Lognormal Distribution 

Figure B-1. Conceptual Comparison of Normal and Lognormal Distributions 

B-4 
For the ratio alb, "a" and "b" are both subject to random variability but both 

are non-negative. Given that "b" is subject to random variability and bounded below only 

by zero, a value very close to zero could occur. The ratio has no upper bound as "b" 

approaches zero; thus the error in the ratio is unbounded above. But the ratio has an 

absolute lower bound of zero. The possibility of values extremely larger than the true 

value, without a corresponding possibility of values extremely lower than the true value, 

tends to cause the uncertainty in the ratio to be asymmetric. 

The method based on the assumption that the ratio is approximately normally 

distributed will be called method 1. The method that produces asymmetric confidence 

intervals will be called method 2. Radian has performed calculations to compare these two 

methods. Tests revealed that method 2 is capable of producing an upper confidence level 

that is Wlreasonably large from an engineering point of view (see the discussion below 

pertaining to separators for the Central Plains Region). The confidence limits produced by 

method 1 under these circumstances are much more reasonable from this perspective. 

Both engineering judgement and further statistical calculations have indicated 

that method 2 is preferable for this application. First, the asymmetric confidence interval is 

based on the general mathematical situation described above, in which the denominator can 

become arbitrarily close to zero. But in our application, the denominator is the sum of the 

production levels or of the numbers of wells for the sites visited in a region. From a 

practical perspective, it is not reasonable to expect that either of these sums can become 

arbitrarily close to zero, causing an extremely large ratio of separators per well or 

separators per unit of production. 

The number of wells at a site of interest must be at least one. Thus, the sum 

of the numbers of wells has a lower bound equal to the number of sites visited. The 

production does not have a definable lower bound of this nature. The argument above still 

applies; however, it is not reasonable to expect an arbitrarily small production rate at all 

visited sites in a region, allowing an unbounded ratio of devices per unit of production on 

the basis of the data for all sites. 

B-5 
The argwnent above pertains to the possibility of an arbitrarily small 

denominator, which could cause extreme skevmess; Figure B-2 depicts a hypothetical 

distribution that is skewed, or asymmetric. The relationship between the number of devices 

and the number of wells or amount of production is also relevant. Theoretically, positive 

skewness in a ratio could result from positive skewness in the numerator; this would be a 

special concern if the numerator could increase without bound, independently of the value 

of the denominator. In this applica:ion, however, it is not reasonable to expect that the 

number of separators attached to a given well is unbounded; similar comments apply for 

other device types. Further, it is not reasonable to expect that the number of separators at 

the visited sites in a region is independent of the total production at those sites and can 

become arbitrarily large, independently of the production level. 

The intuitive arguments above indicate that certain mathematical causes of 

marked asymmetry do not exist in this application. However, these arguments do not prove 

that asymmetry cannot exist at ali. A further investigation was performed on the basis of 

statistical calculations. For each of a selected set of regions and device types, the number 

of devices was divided by the extrapolation parameter (wells or production) for each site. 

This produced a ratio for each site visited for a given region and device type. In most 

cases, the number of sites is too small to allow a detailed characterization of the 

distribution. For separators for the Atlantic/Great Lakes region, however, there were 19 

sites. The distribution of separators per well is displayed for this case in Figure B-3. The 

histogram is somewhat ragged, because of the sample size; even 19 is a small sample size 

to characterize a distribution. Nevertheless, there is no evidence of positive skewness. 

Despite the raggedness of this empirical distribution, a hypothesis test indicated that this 

distribution does not differ to a signifi.cant extent from a normal distribution. 

Figure B-4 presents the histogram for the ;atio of separators per unit of 

production for the same region. In this case, there is evidence of asymmetry ·in the 

distribution of the site-by-site ratios, and the hypothesis test indicated that this distribution 

differed significantly from a normal distribution. The primary reason for the visual 

B-6 
60 

50 ~ 

~ 

>. 

:;::, 40 0 

fJ) 

OJ 

~ 

fJ) 30 c 

+---------------~~ 

----------------~-

.-a-

. ~--------·-···· ~ 

0 

• fJ) 

..... "' fJ) 20 .... ' .E 

w 

1:t= ......................... = ...................... ·-=:: .............................. ::-- ............... :==::~ 

0 10 20 30 40 50 60 70 60 00 100 

Uncertainty (%, Normal Assumption) 

--A--- Emissions ......,.._.. Normal Limit --e-- Lognormal Limit 

Figure B-2. Comparison of 90% Confidence Limits (Normal and Lognormal Assumptions) 
;; ~ 

v j 

' 

l 

' 

5 i 

' 

j 

4 i 

1 

~ 

~ 

z 

~ 

=> 

3 0 

~ 

"' 

~ 

j 

2 i 

j 

o.oo 0.25 0.50 0.75 1.00 

Separators/Well 

Figure B-3. Distribution of Separators per Well 

B-8 
I 0 1 

91 

R j 

u 1 

7 j 

~ 

~ 6 

z 

~ 

~ 

0 

~ 

~ 

~ 

10 30 50 70 90 

Separators/Production (MMcfd) 

Figure B-4. Ratio of Separators per Unit of Production 

B-9 
impression of asymmetry is a single site with a ratio of 99.9 separators per MMcfd of 

production. Asymmetry in the site ratios, however, does not necessarily imply that the 

error in the ratio for the region is asymmetrically distributed. For the site with the large 

ratio, there are I ,582 separators and 16 MMcfd of production. Another site with a more 

moderate ratio has a much larger impact on the ratio for the region. This site has 

3,227 separators and 81 MMcfd of production, so the ratio is 39.8 separators per MMcfd. 

The site values of the number of separators per we!l for the Central Plains 

region revealed evidence of negative skewness. That is, instead of a long tail to the right, 

as in Figure B-4, there was some evidence of a long tail to the left. Since there were only 

seven sites, a histogram of this data set would not be meaningful and is not shown. In this 

case, method 2 produced an upper confidence limit for the ratio of separators per well that 

was unreasonably large in this case from an engineering or a statistical point of view. This 

upper limit was several times the largest site ratio of separators per well for the Central 

Plains region and exceeded almost all the separator-per-well site values for several regions. 

In this example, method I produced results that were considered to be much more 

reasonable. While negative skewness was the exception, this example provides another 

illustration of why method I was preferred over method 2. 

Moreover, asymmetric uncertainties of individual parameters exist for other 

reasons. Confidence intervals with greater than IOO% uncertainty exist for activity factors, 

emission factors, or emission rates for some source categories. One possible explanation is 

that the error in the estimated parameter is not nonnally distributed. The ultimate objective 

of the study, however, is to estimate the national emission rate. The sum of the emission 

rates for 82 source categories will tend toward nonnality, even if some of the individual 

values summed are nonnonnal. Thus, even if some category parameters were not nonnal, 

this would not necessarily invalidate the confidence interval for the national emission rate. 

Moreover, an assessment has been made of the effect of a lognonnal error in the industry 

emission rate. The upper confidence ;imits based on the normal and lognormal assumptions 

differ by a small amount, and the target precision is met on·the basis of either· assumption 

(Section A.4). 

B-10 
Based on a finite sample of size n (i.e., n sampled sites), the following is an 

approximation of the variance of the error in Y R: 

The quantity N, the total nunber of sites in the region, is not known and, 

therefore, must be estimated. The total number of separators, X, in the region is known. 

The quantity X divided by the average number of separators per site is an approximation of 

the number of sites in the region. This me:hod of estimating N was suggested to Radian by 

Jonathan Cohen of ICF Kaiser in a private communication. 

Thus, N is an estimate rather than a known constant. The value .N is used 

only in quantifying the uncertainty of Y R• however, and not in estimating "?" R· The quantity f 

is the sampling fraction, nJN. 

The equation given by Cochran for a symmetric confidence interval for Y R is 

as follows: 

where z is a tabulated value of the standard normal distribution selected according to the 

confidence level; for a 90% confidence interval, the z value is 1.645. The z value is 

appropriate when the quantity estimated ('9' J has an uncertainty, but the uncertainty of the 

variance [v(YJJ can be neglected. The use of the z statistic is generally accepted if the 

sample size is greater than 30. 

According to Cochran's rules of thumb, tht! sample size would be at least 30 

when this expression for the confidence interval was used. In our case, however, the 

decision that the symmetric confidence interval was preferable to the asymmetric 

confidence interval even if the sample size was less than 30 was based on engineering 

B-11 
considerations and data analysis, as is discussed above. To account for the uncertainty in 

the variance as well as in the eStimate, we have replaced z in the expression above by the 

appropriate t value. Even though the !-distribution does not apply exactly in this context, 

replacing z by t provides a degree of conservatism; that is, somewhat wider confidence 

intervals are produced, which tends to account for the uncertainty in v(Y J. The resulting 

confidence interval is as follows: 

r, ± t Jvcr,) 

B.2 COMBINATION OF ESTIMATES OF AN ACTIVITY FACTOR 

The methods discussed in the preceding section were used to est.irnate "the 

activity factor and its uncertainty on the basis of both well counts and production. The 

arithmetic average of the two estimates was computed to obtain the final estimate. 

The two estimates are based on different extrapolation factors (values of the 

x) but common device counts (values of the y;). The device counts vary by site and are 

subject to sampling error. Thus, this source of sampling error was conunon to the two 

estimates of the activity factor. It has been discussed elsewhere that separate measured 

quantities (e.g., emission rates from different types of devices) may have correlated 

sampling errors. The evidence her~ for correlation is much stronger, however, since 

common data are used in the two estimates. Thus, steps were taken to account explicitly 

for the correlation. To address this issue, we introduce the following notation: 

number of wells at the ilh site; 

production at this site; 

= estimate of R on the basis of wells; 

B-12 
R, estimate of R on the basis of production; 

YR.w ~ estimate of Y R on the basis of wells; and 

Y~, ~ estimate of Y R on the basis of production. 

By substituting xw ., for X; in the appropriate equations in the preceding 

section, for example, one obtains the estimate of the number of devices in the region on the 

basis of wells and the confidence interval for this estimate. The following is a sample 

estimate of the covariance between the errors in the two estimates: 

This expression satisfies important required properties of the covariance, such as the 

symmetry property: 

Additionally, the covariance between a quantity and itself equals the variance of that 

quantity. This can be confirmed simply by replacing all "w" subscripts with a "p" 

subscript, to obtain the variance of ."9' p· 

In a textbook application of the ratio method, the quantity N would be 

known. As discussed earlier, N must be estimated in this application. In estimating the 

covariance above, the average of the two estimates of N was used, both where N appears 

explicitly in the covariance equation and in calculating f. 

Now, the expression for the final estimate of the activity based on the 

arithmetic average approach is as follows: 

The variance of this expression IS: 

B-13 
y 

R,avg 

= var(Y .)+2cov(YR~' YR,J+var\YR,J 

4 

The confidence interval for the final estimate is as follows: 

In some instances, the number of sites for which data existed for both wells 

and production did not coincide exactly. In these cases, the covariance was computed on 

the basis of the sites for which com .'non data did exist. This provided a somewhat 

conservative (large) estimate of the covariance. This calculation of the covariance 

represents the case in which the sites in common for the two extrapolation parameters are 

the only sites. But the fact that sites exist with data for wells but not production (or vice 

versa) introduces an element of independence between the estimates of Y R based on the two 

extrapolation parameters. The somewhat conservative covariance estimate produces a 

somewhat conservative confidence interval for the final estimate of Y R· 

To account for this case, the correlation between the errors in the two 

estimates was computed as follows: 

follows: 

r = cov(Y R.w' Y R,p) 

Jvar(YR,Jvar(Y.) 

Then the half-width of the confidence interval for ?" R was computed as 

where tP and ~v are the t-values appropriate for the sample sizes for the two extrapolation 

parameters. The expression involving the correlation coefficient was written in the manner 

B-14 
shown to emphasize that this is approximately an error propagation using half-widths of 

confidence intervals. Each t-value is grouped with its respective standard error (the square 

root of an error variance is a standard error). The expression above can be simplified 

algebraically to the following: 

This expression involving different sample sizes for the two estimates reduces 

to the simpler expression for the half-width of the confidence interval given earlier if the 

sites for which data exist for wells and production are the same. 

B-15 

APPENDIX C 

Conversion Table 

C-1 
Unit Conversion Table 

English to Metric Conversions 

1 scf methane ~ 19.23 g methane 

1 Bscf methane ~ 0.01923 Tg methane 

I Bscf methane ~ 19,230 metric tonnes methane I Bscf ~ 28.32 million standard cubic meters I short ton (ton) ~ 907.2 kg I lb = 0.4536 kg I ft' = 0.02832 m' 

I ft' = 28.32 liters 

I gallon = 3. 785 liters 

I barrel (bbl) = 158.97 liters 

I inch ~ 2.540 em 

I ft = 0.3048 m 

I mile = 1.609 km 

I hp = 0.7457 kW 

I hp-hr = 0.7457 kW-hr 

I Btu ~ 1055 joules I MMBtu ~ 293 kW-hr 

I lb/MMBtu ~ 430 g/GJ T ("F) ~ 1.8 T ("C)+ 32 

I psi ~ 51.71 mm Hg 

Global Warming Conversions 

Calculating carbon equivalents of any gas: 

MMTCE = (MMT of gas) x ( MW, carbon) x (GWP) 

1\1W, gas 

C-2 
Calculating C02 equivalents for methane: 

MMT of C02 equiv. = (MMT CH,) X X (GWP) (

MW, co,) 

MW, CH4 

where MW (molecular weight) of C02 ~ 44, MW carbon ~ 12, and MW CH, ~ 16. 

Notes 

scf ~ Standard cubic feet. Standard conditions are at 14.73 psia and 60'F. 

Bscf ~ Billion standard cubic feet (1 09 set). 

MMscf ~ Million standard cubic feet. 

Mscf ~ Thousand standard cubic feet. 

Tg ~ Teragram (1012 g). 

Giga (G) ~ Same as billion (1 09

). 

Metric tonnes ~ 1000 kg. 

pstg ~ Gauge pressure. 

psra ~ Absolute pressure (note psia = psig + atmospheric pressure). 

GWP ~ Global Warming Potential of a particular greenhouse gas for a given 

time period. 

MMT ~ Million metric tonnes of a gas. 

MMTCE ~ Million metric tonnes, carbon equivalent. 

MMT of C02 eq. ~ Million metric tonnes, carbon dioxide equivalent. 

C-3 
TECHNtCAL REPORT DATA (Pitmt! rud llll:tructiOns on tht: TnUrt! M{Ol't! complt:tiltg} 

1. REPORT NO. 

I' EPA-600/R-96-080e 3. RECIPIENT'S ACCESSION NO. 

4 TITLE ANOSVBTITLE Methane Emissions 5. REPORT DATE from the Natural Gas Industry, 

Volumes 1-15 June 1996 (Volume 5: Activity Factors} 6. PERFORMING ORGANIZATION CODE 

7. 

~erson, 

AuTHORCSl 

M. 

L. 

HalL 

Cam~bell. M. Cam/bell, M. Cow~ll, D. Ep- B. PERFORMING ORGANIZATION REPORT NO. I 

• Harrison, • Hummel. D. yers. DCN 96-263-081-17 I 

• Shires, B. Stapper, C. Stapper. J. Wessels. and * "·PERFORMING ORGANIZATION NAME AND ADDRESS Radian Inter:c:ational LLC 10. PROGRAM ELEMENT NO. 

P. 0. Box 201088 

11. CONTRAC /GRANT NO. 

A us tin. Texas 78720-1088 5091-2 51-2171 (GRI) 

68- Dl-0031 (EPA) 

12, SPONSORING AGENCY NAME AND AOOFIESS 

EPA, 

13. TYPE OF R'POAT iNC PERIOD COVERED 

Office of Research and Development Final; 3 91-4 96 

Air Pollution Prevention and Control Division 14. SPONSORING AGENCY CODE 

Research Triangle Park, NC 277!1 EPA/600/13 

Cosponsor 

ts.suPPLEMENTAAYNOTes 

GRI 

EPA project officer is D. A. Kirchgessner. MD-63. 919/541-4021. 

Mawr Ave., Chicago, project officer is R. A. Lott, Gas Research Institute, 8600 West Bryn 

IL 60631. (*)H. Williamson (Block 7). 

16. ASSTRACTThe 15-volume report summarizes the results of a comprehensive program 

o quantify methane {CH4) emissions from the U.S. natural gas industry for the base 

~ownstream 

lvear. The objective was to determine CH4 emissions from the wellhead and ending 

at the customer's meter. The accuracy goal was to determine these 

~e 

temissions 

1992 

within +/-0. 5% of natural gas production for a 90% confidence interval. F01 

+I- 105 Bscf 

base year, total CH4 emissions for the U.S. natural gas industry was 314 

gas production. 

(6. 04 +I- 2. Ol Tg). This is equivalent to l. 4 +/- 0. 5% of gross natural 

Gas Association/EPA 

and reflects neither emissions reductions (per the voluntary AmeriStar Program) nor incremental increases (due to increased 

gas usage) since 1992. Results from this program were used to compare greenhouse 

bas 

ming 

emissions 

potentials 

from the fuel cycle for natural gas. oil. and coal using the ·global warChange 

(GWPs) recently published by the Intergovernmental Panel on Climate 

(IPCC). The analysis showed that natural gas contributes less to potential 

by 

global warming than coal or oil. which supports the fuel switching strategy suggested 

the IPCC and others. In addition. study results are being used by the natural gas 

industry to reduce operating costs while reducing emissions. 

"· KEY WORDS AND DOCUMENT ANAl.. YSIS 

•• DESCRIPTORS b.IDENTIFIERS/OPEN ENDED TERMS '· COSA Tl Field/Group 

Pollution 

Emission 

jPollution Prevention 13B 

Greenhouse 

Stationary Sources 14G 

Effect Global Warming 

Natural Gas 

04A 

Gas Pipelines 

2lD 

Methane 

15E 

~7C 

18. OISTRJSUTJON STATEMENT 19. SECURITY CLASS (ThU Rt:pDn} 21. NO. OF PAGES 

Release Unclassified 137 

to Public 20. SECURITY CLASS {ThU pt>gt:) 22. PRICE Unclassified I 

' EPA Form 2220·1 (9·73) C-4 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

Office of Research and Development 

National Risk Management Research Laboratory 

Technology Transfer and Support Division 

Cincinnati, Ohio 45268 

OFFICIAL BUSINESS 

PENALTY FOR PRIVATE USE. :5300 

AN EQUAL OPPORTUNITY EMPLOYER 

If your 6ddrus is incorrect. pi~II!St change on the above label 

tear off; and teturn to the above 11ddress. 

If you do not desire to continue receiving these teclfnical 

reports. CHECK HER£0; tear off label, and return it to the 

above address. 

Publication No. EPA-600/R-96-0BOe