### FOREWORD The U.S. Environmental Protection Agency is charged by Congress with protecting the Nation 1S land. air, and water resources. Under a mandate ofnational environmental laws. the Agency strives to formulate and implement actions leading to a compatible balance betweenhumar. activities and the ability of natural 

### systems to support and nurture life. To meet this mandate, EPA's research program is providingdata and technical supportfor solving environmental problems today and building a science knowledge base necessary to manage our ecologicalresources wisely, understand how pollutants affect ourhealth, and prevent or reduce environmentalrisks in the future. The National Risk Management Research Laboratoryis the Agency•s centerfor investigation of technological andmanagement approachesfor reducing risks from threats tohumanhealth and the environment. Thefocus of the Laboratory•s research program is onmethodsfor the prevention and controlofpollution 

### to air.

**land~** water. and subsurfaceresources;: protection ofwater qualityinpublic water systems; remediation of contaminated sites andgroundwater: and prevention and control of indoor air pollution. The goal of this research effort is 

### to catalyze development andimplementation of innovative. cost-effective enVironmental technologies; develop scientific and engineeringinformation needed by EPA to supportregulatory and policydecisions; and provide technical support and infOrmation transfer to ensure effectiveimplementation of environmentalregulations and strategies. 'This publicationhas beenproduced as partof the Laboratory•s strategic longterm research plan. Itispublished and made
**av~able** byEPA 1s Office of Research and Developmentto assist the user community and to link researchers with their clients. E. TimothyOppelt. Director NationalRisk Management Research Laboratory 

### EPAREVIEWNOTICE Thisreport hasbeen peerandadministrativelyreviewed bytheU.S. Environmental Protection Agency, and approved for publication. Mention of trade names or commercialproductsdoesnotconstituteendorsementorrecommendation foruse. Thisdocumentisavailabletothepublicthrough the NationalTechnicallnformation Service, Springfield,Virginia22161.METHANEEMISSIONS FROM THENATURAL 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 ProjectManager: 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 ofwork sponsored by Gas Research Institute (GRI) and the U.S. Environmental Protection 

### Agency {EPA). Neither EPA, GRI, members ofGRI, nor any person acting on behalfof either:

a.

b. Makes any warranty or representation, express or implied, with respect to the accuracy, completeness, orusefulness ofthe information contained in this report, orthat the use ofany apparatus, method, orprocess 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 ofResearch and Development quality assurance/quality control (QNQC) requirements are applicable to 

### some ofthe 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 ofexperts listed in Appendix DofVolume 2.

iiTitle
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 ofnatural 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 ofenergy produced than 

### either coal or oil. On the basis ofthe amoturt ofC02

### emitted, the potential for global wanning could be reduced 

### by substituting natural gas for coal oroil. However, since natural gas is primarily methane, a potent greenhouse gas, losses ofnatural gas during production, processing, transmission, and distribution could reduce the inherent advantage ofits lower C02 emissions. To investigate this, Gas Research Institute (GRJ) and the U.S. Environmental Protection Agency'sOffice ofResearch and Development 

### (EPNORD) cofunded a major study to quantify methane emissions from U.S. natural gas operations for the 1992 base year. The results ofthis study can be used to construct global ID:ethane budgets and to determine the relative impact on global wanning ofnatural 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 ofthe industry. iii

### Technical Approach

Project Implications The program reached its accuracy goal and provides an accurate estimate ofmethane 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 ofscaling up the sampled emissions from a representative set ofsources 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 ofsimilar 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 ofsources in the entire target population or source category. Thus, the product ofthe 

### 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 ofthe activity factors is dependent upon precision and bias. The precision ofthe 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 ofthe 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'sOffice ofAir 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 IVmethane emissions from this program are not reflected in this study's total emissions.

Robert A. Lott Senior Project Manager, Environment and Safety



### vTABLE 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 ofActivity and Emission Factors . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Effect ofSampling Approach on Activity Factors
. . . . . . . . . . . . . . . . . . 15 4.3 Activity Factor Sources
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 6 4.4 Extrapolation ofSite Activity Factor Data
. . . . . . . . . . . . . . . . . . . . . . . 17 4.4.1 Selection ofSite-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 ofError ................................. 24 4.5.3 Screening for Bias .................................. 26 5.0 PRODUCTION SEGMENT ...................................... 28 5.1 Development ofa 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 ReliefValves ............................ 40 5.8 Gathering Pipeline Miles ................................... 46 5.9 Maintenance Activities ..................................... 48 5.10 Compressors and Dehydrators
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50



### vi

### TABLE OFCONTENTS (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 ofPipeline ......................................... 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 OFFIGURES 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 ofNonnal and Lognormal Distributions . . . . . . . . . . . B-4 B-2 Comparison of90% Confidence Limits
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-7 

### B-3 Distribution ofSeparators per Well .................... · . . . . . . . . . . . . B-8 B-4 Ratio ofSeparators perUnit ofProduction
. . . . . . . . . . . . . . . . . . . . . . . . . . B-9

viii

### LIST OFTABLES 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 ReliefValves ......................................... 46 

### 5-12 Production Gathering Pipeline Mileage

- Gas Wells
. . . . . . . . . . . . . . . . . . . . . 48 6-1 Divisions ofGas 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 ofLeak Record Data from Participating Companies .............. 83 9-3 Summary ofActivity Factors for Distribution Underground 

### Pipelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 9-4 Activity Data Provided by Individual Companies . . . . . . . . . . . . . . . . . . . . . . . 86 9-5 Summary ofActivity Factors for Distribution Metering/ Pressure Regulating Stations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 

### !0-I Well-Defined Activity Factors .................................... 90 I0-2 Examples ofDeveloped Activity Factors ........ _

..................... 91 A-I Production Sites (Summary) .................................... A-2 A-2 Production Sites (Offshore Data) ................................. A-3 A-3 Production Sites (GulfCoast 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

XI1.0 

### SUMMARY This document is one ofseveral volumes that provide background information supporting the Gas Research Institute and U.S. Environmental Protection Agency Office ofResearch and Development (GRJ-EP

### NORD) methane emissions project. The objective ofthis comprehensive program is to quantify the methane emissions from the gas industry for the 1992 base year to within±0.5% ofnatural gas production starting at the wellhead and ending immediately downstream ofthe customer's meter. Activity factors are needed to estimate the national emissions from the natural gas industry and are based on estimated populations ofequipment and equipment characteristics. This report presents a definition for activity factors, methods ofcalculation, and this project's calculated values for industry activity factors.

I

### 2.0 INTRODUCTION Ifemissions 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 ofthe size ofthe industry, measuring emissions from each source is impractical. Therefore, a method ofscaling up (i.e., extrapolating) the sampled emissions from arepresentative set ofsources 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 ofsimilar 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 ofsources 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 orper 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 ofthe industry. This report discusses precautions taken to ensure that the data used to 

### detennine activity factors were statistically representative ofthe 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 ofthe gas industry. Summary results are presented in Section 10. This report is one ofseveral volumes under the GRI/EPA methane emissions project.



### 33.0 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 ofspecific equipment. This section ofthe report characterizes the industry by outlining the segments ofthe industry as well as the types of equipment found within each segment. \Vhile this section draws a general picture ofthe 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 ofthe operation are wmecessary for development ofan 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 ofthe gas by local distribution pipeline networks to the customer. The generally accepted segments ofthe 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 ofthe industry that specify what equipment is included or excluded from the study. These boundaries were set 

### 4I 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 GasPlant

i I 

## c Liquids Storage Liquids
Underground Storage Reservoir



### Figure3-1. GasIndustryFlowCbart 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 I I I I I I I I I 

### DISTRIBUTION

M&PR Stations Main and Service Pipelines

CustomerMeters 

# 0 Compressor @ Meter

**~** Pressure l*f Regulatorusing input from industry experts. The guideline used for setting the boundaries was to exclude equipment in each segment not required for the marketing ofnatural 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 ofpropane, 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 ofmethane that result from oil production, orthat 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 ofthe gas industry. The production segment is composed ofgas 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 ofmarketing 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)

CoalBed MethalleWe!!

GasWell Wellhead CbcrrucaJ !n;e<UOn Pump 

### r>-- Petroleum industry

Compressor



# f---~·,;h~~o.

**~** FoeldUse

Gu .------- -

## ~ VaporRecovery

**~** Compres.sor
PetroleumIndustry

Figure 

### 3-2. Industry Boundaries


### 7exists primarily for the production ofoil, since 

### it would exist even ifthe 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 ofthe 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 ofthe liquid piping are considered part ofthe oil industry and are excluded from the GRI/EPA gas industry study. The gas industry equipment begins only on the gas line do\oVllstream ofthe separator, at the first piece ofgas line equipment, such as the sales meter, compressor, or dehydrator. In general, an oil oroil and gas field may have centralized surface treatment facilities, oreach well site may have its own independent surface facilities. In centralized facilities, all ofthe 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. Forexample, separators may be at each well (decentralized), while compression and dehydration may be centralized. Whatever thefield configuration, all gas wells have a wellhead and most have a gas meter. Also independent ofthe field configuration, gas wells may ormay not have separator(s), a dehydrator, or a compressor. The use ofthe 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 ormay 

### 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 orco-produced gas that is marketed. Therefore, some ofthe equipment at these free-flowing oil wells is considered part ofthe gas industry ifit 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 pwnpjacks usually do not produce or market any gas and therefore are 

### not part ofthe 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 ofthe amount injected are considered part ofthe 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) ofthe 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 ofthe product stream. The back end ofthe 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 ofthe gas plant has negligible methane emissions since the liquids handled contain only trace amounts ofmethane. The front end ofthe gas plant often contains dehydration facilities, wet gas compression, and the absorption or compression and cooling process. All gas plants are considered part ofthe 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 GulfCoast to the Eastern seaboard ofthe United States. The segment consists oflarge diameter pipeline, compressor stations, and metering facilities. All ofthese facilities and all ofthe equipment they contain are considered part of the natural gas industry. Transmission compressor stations usually consist ofpiping manifolds, reciprocating or gas turbine (centrifugal) compressors, and generators. Dehydrators may be included but are not usua1Iy present because ofupstream drying. The station may also include metering facilities. Figure 3-4 shows a schematic diagram oftransmission and storage stations.

10

## c

Dehydration Absorption or Compression & Cooling Process

Liquids Backendofthe 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 

# ---------------
Figure3-3. GasProcessingPlant !I

# - N Gas from Pipeline - 

### ' TRANSMISSION ABOVE GROUND STORAGE COMPRESSOR STATION 

# fD Cooling



# ~ LNG Compressors Process Tank 

## c Heaters

**~** 

# 1 c Gas Transmission Pipeline 

## c from Pipeline 

### BELOWGROUNDSTORAGE 

## c Compressor 

## c



# $~~ Station

Wells



## ~ **~ ~**  Dehydrator Storage Field
Figure

**3~4.** Transmission and Storage StationsTransmission 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 ofseveral fonnations: 1) spent gas production fields, 2) aquifers, or 3) salt caverns. Below-ground storage is the predominant means ofgas storage. Most storage stations

**con~ist** ofa 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 ofthe residential, commercial, and industrial consumers. The segment includes pipeline (mains and services), meter and regulating stations (city gates), and customer meters. All ofthese facilities are considered to be an integral part ofthe gas industry. Figure 3-5 shows a schematic ofthe distribution segment and associated equipment.

13Meterand Pressure Pressure-Reducing Regulating (M&PR) Station Station Mains(Pipeline)



## ~ t?. 

## --.. M Transmission 

### t?. Pipeline Meter Pressure-Reducing Regulators



### Figure3-5. DistributionSegmentEquipment Services (Small Pipe) M M M M M M M M M CustomerMeters (Residential, Commercial,

Industrial, Electric Uli!ity)4.0 DEVELOPMENT OF ACTIVITY FACTORS This section discusses the sampling approach, sources ofequipment counts, and methods used to develop activity factors. 4.1 Definition ofActivity and Emission Factors The activityfactor is typically defined as a count ofthe total population of sources in a particular category, and the emissionfactor 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 ofthat 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 ofoperation. (For example, the emission factor for internal combustion (IC) 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 ofSampling 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 ofactivity faCtors. Improper sampling can easily bias the results. Forthis reason, particular attention was paid to the sampling methods used for this project. However, an absolutely random sample ofthe natural gas industry could not be ensured because participation in this program was voltmtary.
In addition to ensuring as random ofa sample as possible, the project team attempted to identify and compensate for any biases in the data from the sampling. For example, ifregional differences in equipment configurations were kno\VIl to exist, then data 15were 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 ofthe biases identified by adjusting the sampling method used. For the example ofregional bias, the United States was divided into six regions to account for the regional differences in most ofthe activity factors, and samples were taken in each ofthose regions (see Section 5 for more details). No significant regional differences were observed in the major activity factors in the gas processing, transmission, ordistribution 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, orthrough site visits. Organizations such as A.G.A. track national statistics such as gas wells, miles oftransmission and distribution pipelines, and total

**n~tional** production in the naturil gas industry. Table 

### 4-1 shows some ofthe published and well-defined activity factors. However, 

### in many cases, the total population ofa 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 tobe gathered to comprise arepresentative sample to extrapolate to a national population. 16TABLE 4-1 NATIONALLY-TRACKED ACTIVITY FACTORS 

# . Segment Activity Factor Name Number Tatal Industry 1992 Gross Gas Production (Tscf) 22.13 1992 Marketed Gas Production (Tscf) 18.71


### . Production No. ofGas Wells 276,000 No. ofOil Wells 602,200 Processing No. ofGas Plants 726 Transmission and Storage Miles ofTrans Pipeline 284,500 No. ofStorage Facilities 475 No. ofStorage Wells 18,000 Distribution Miles ofMains 836,760 No. ofServices 51,520,000
Forsources that have an unknown population, a numberof

### site visits 

### were conducted to determine the numberofsources at each location. The average valuesIrom multiple sites were then scaled-up to represent the total population. Site visits to collect activity factor data were typicallydone in conjunctionwith the data collection efforts for the emission factors. Forthe 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 ExtrapolationofSiteActivity Factor Data In the GRJ/EPA study, some activity factors were determined based on extrapolative site data. Forexample, no data were available onthe nationwide populationof

17

### production separators. The numberofseparators at a site, gathered as part ofsite visits, was divided by the numberofwells at each site. Then, the average ratio ofseparators to wells from all site visit data was used to extrapolate nationally by multiplying by 

### the national well count. This method ofscaling-up the site visit data uses 

### extrapolationparameters (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 ofthe nationally-tracked activity factors presented in 

### Table 4-1. Equipment activity factors from one individual site'sdata (AF.J canbe extrapolated to regional ornational activity factors (AFR orAFN, respectively) by using EP. Examples ofextrapolationparameters are the number ofwells for production and storage, the number ofplants forprocessing, and gas throughput for all segments ofthe industry. To scale-up a site activity factor to the regional ornational level, the site activity factor must be 

### divided by 

### theextrapolationparameterfor that site and multiplied by 

### the regional ornational value for the extrapolationparameter. Ineffect, the site activity factor (AF 5) 

### is being multiplied by 

### the ratio oftwo known factors: one onthe site basis 

### (EP,) and the otherat the regional (EP.J level. This is illustrated in the following equation: (2) 

### Detennining the ratio ofthe site activity factor to the site extrapolation parameter for multiple sites withina region can bedone using one oftwo equations: a siteweighted method oran approach usinganaverage countper site (not site-weighted). The first uses a ratio ofthe summationofactivity factors for all sites to the sununation of extrapolationparameters for all sites:

18

### " 

# I: AF, Method 1: (3) (AF/EP), = i • I 

## " (Site-weighted) 

# I: EPi i • ! where n = number ofsites. The second is an average ofindividual site ratios. This equation detennines the ratio to be used for scale-up, (AF/EP)s, by summing the ratio ofthe site activity factor to the extrapolationfactor, (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 SelectionofSite-Weighted(Ratio)ExtrapolationTechnique The site-weighted method (Equation3) was selected for the extrapolation technique for all production activity factors. Inproject review meetings, this technique was accepted by productionindustry representatives and theircompany statisticians as the best approach. The site-weighted approach is based onthe assumption that the sites are randomly sampled, so that a large site is proportionately representative ofa large section ofthe 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 initiallyused in earlier drafts ofthis 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 ofa heterogeneous large field (!0,000wells), while a large site (100 wells) might be the full sample ofa 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 inthe gas processing, transmission, and storage segments. 19

### 4.4.2 Extrapolation Parameter Selection 

### Some equipment activity factors can be scaled up by any ofseveral available extrapolation parameters (EP). These parameters must be known for each site as well as for the national orregional total. Forexample, in the production segment there are only three extrapolation parameters: 1) gas well count, 2) gas productionrate, and 3) total "gas industry" well count. Gas wells are simply the total count ofgas wells at the site, and gas productionrate is the rate ofgas marketed from the site. Both ofthese values are well known for the nation and for the regiOn. "Gas industry" wells are the sum ofgas 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 numberofoil wells marketing gas is not known. Nevertheless, the number ofoil wells marketing gas was determined to a reasonable degree ofaccuracy (see Section 5.5). Ifthere was aphysical orteclmical relation between the equipment and one of the three candidates for the extrapolationparameters (EP), then that parameter was selected. 

### This was the case, for example, for in-line heaters and chemical injectionpumps, which are related to well count based onengineering design reasons. However, where the relation between the source population and various extrapolationparameters is not definitive from a technical perspective, a single EPcannot be 

### selected. Therefore some alternate approaches forcombining the various EP'scan be used, such as: 1) averaging the results from both methods ofEPextrapolation, or2) statistically analyzing the data to determine the best approach. 

### Forexample, 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 ofmetering/pressure regulating stations by 

### miles ofmain pipeline orby system throughput. which were the onlyknown population statistics. The station counts from individual companies were examined both on aper mile ofmain and per
20system 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 equipmentand the three extrapolation parameters (gas well counts, total well counts, or productionrate). The EPselected was based upon Radian's and the industry reviewer's knowledge ofproduction operations. Ifthe equipment was most strongly related to one parameter, and not related orvery weakly related to the otherparameters, then the first parameter was selected. Ifthere waS a rationale for a strong technical relation between both, then an average ofthe approaches was used. In several cases, the total "gas industry well" count could be excluded as an EP ifthe boundaries selected (see Figure 3·2) excluded that equipment on associated oil wells. Inmany cases, there was a reason to expect that equipmentcounts were related to both wen counts andproductionrate. This was true for pneumatics, separators, and compressors. Therationale for relation to bothparameters on this equipment is as follows. Surface facilities may be organized as central facilities orindividual wen site facilities. 

### If surface facilities are sited on individual well sites, which occurs frequently, then a relation between equipmentcount and wells would be expected. Ifthe equipment was sited at central facilities, then it mightbe specified and sized based purely uponproductionrate. Sinceboth relations clearly existed, the average was selected. The production activity factors, extrapolationparameters, and the basis for the selection are listed in Table 4-2. 

### 4.5 Activity FactorAccuracy Determiningthe accuracy ofthe activity factor is a key panofestimating the accuracy ofthe national emissionrate. Accuracy is dependent on precision and bias. as discussed in Volume 3 on general methodology! and Volume4 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 heaters exist only on gas wel!s and have a strong relation to well count. While some heaters (such as heater-treaters) doexist on oil wells, they are not within the boundaries ofthe gas industry (see Figure 3-2). Separators Mean of: I)gas well and 2) production rate. Separators can be related toboth production rate and well count Separators can Exception: offshore, where the relation is to be at individual wells, orcan be at central facilities, where production rate may production rate only. play a factor. In the offshore area, the relation is stronger to production rate. Compressors 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 there is a relation to total well count. 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 gas wells. Gas lines from any gas producing well may have dehydrators and compressors with pneumatics. Therefore pneumatics are related to total gas industry well count. Pneumatics can also be related closely with production rate where central separation facilities exist. (The siting ofequipment and pneumatics atcentral 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 ofthe 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 ofmeasurements. However, 

### bias, a systematic error in the estimate, must be discovered and eliminated. 

### 4.5.1 Precision Ifthe activity factor estimate is assumed to be approximately normally distributed, then the 90% confidence limitsfor 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 ofequipmentat site i in the sample set; xi = the numberofwells oramount ofproduction at site i in the sample set; n 

### = numberofsites sampled 

### in the given region; N = the total number ofsites in the region; f = sampling fraction = n!N; R = activity factorratio = (AF/EP)samp1 e; and t0_a12•n-1} = the 1-a/2probability ofthe Student'st Distribution with n-1 degrees offreedom.

23 (5)

(6)

### The total number ofsites (N) is not known for each region ornationally. Thus, 

### itmust be estimated by the following equation: N= 

### (Production or Wells)total,regton (Production or Wells)10

### tal.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 ofError This section discusses the general techniques used to propagate the error bounds {forprecision) that are calculated 

### in Section4.5.1. The error bounds oftwo numbers can be propagated to determine the error bound oftheir sum and/or oftheirproduct. These techniques are covered in more detail in Volume 4 on statistical methodology,3 

### but are summarized here for the reader'sconvenience. Products are oftenused in this study since the basic extrapolation technique was to take 

### the product ofAF x EF to obtain the 

### source's emission rate {see Section 2). Sums are alsoused frequently since all ofthe individual source emission rates are summed to obtain the national annual emissions from the natural gas industry. Section4.

### 5.I discussed the calculation

### of90% confidence halfwidths for a single term, such as an emission factor oractivity factor. These confidence halfwidths can be plugged into the following equations to

### determine the confidence bounds for sums and products. Foruncorrelated values, the errorbound (90% confidence halfwidth) ofa sum is the square root ofthe sum ofthe squares ofthe absolute errors ofthe values being summed. An illustrative example follows. Supposethe following values, Aand B, are to be summed, and that the confidence bound ofvalue "A" is expressed as "a" (in absolute terms). The bottom cell ofTable 4-3 shows the resulting errorcalculation for the sum. 

### 24

### TABLE 4-3. ERROR PROPAGATION FOR A SUM 90% Confidence HalfWidths 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 ofsum = square root of(az + b:)
Forcorrelated values, the equation for errorbecomes (a2 

### + b2 

### + 2rab)112, where r = correlation coefficientbetween A and B. However, r was assumed to be zero for most categories since they were derived from different data and were unrelated. Theerror bound (90% confidence halfwidth) on a product is also calculated with 

### the absolute errors ofthe 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. Thebottomcell ofTable 4-4 shows the 

### resulting errorcalculation for the product. TABLE4-4. ERRORPROPAGATIONFOR APRODUCT 90°/o Confidence HalfWidths Values to be

Multiplied Absolute Value Fractional Value A a fa= a/A B b fb= b/B Product = A x B relative error ofproduct = square root of[fa2 + tb2+(fa2 x fb2)]



### 254.5.3 Screeningfor Bias It 

### is impossible to prove that there is no bias in any data set. While tests can be designed that are capable ofrevealing somebias, there are no tests nor group oftests that 

### will reveal ali possible biases. Assuming that a data set has no bias, even afterextensive 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 identifiablebiases. Three basic methods were used to screen forbias: peer review ofthe activity factors 

### byexpei-ienced sources, analysis ofthe data, and extrapolation bydifferentparameters (EPs). Methodsforgathering the data were tested repeatedly through extensive technical and industrial review. Numerous individual reviews and project advisor'smeetings overa number ofyears were used toexamine theproject data with industryrepresentatives and otherexperts so that systematic errors could be identified and eliminated. Whenpossible biases 

### in the activity factor samPlingplan orextrapolationmethod were theorized; the project was altered to test for that bias and eliminate it if

### it existed. Oneexample ofthis review process is the identification ofregional differences 

### inproductionpractices. These differences were identified during the advisormeeting review process. The regional bias was then overcome by subdividing the production data into two offshore and four onshore regions, sampling within eachregion, and extrapolating by region. Some ofthe production data that were collected and analyzed were also checked for bias by extrapolatingthe activity factors with multipleparameters (i.e., EPs)_ Fora subset ofdata that is perfectly representative ofthe gas industry, equipment counts from the data set could be extrapolatedto national totals by any variable in the data set. Any extrapolationfrom the perfect subset ofdata would deliver the correct answer, regardless of the parameter used. Foranimperfectdata set, which all data sets are, extrapolationby multiple variables allows for a cross check forbias. Forexample, inproduction, the equipmentcounts can be extrapolated by productionrate orwell count. These two methods 26

### oftenproduced different answers that were averaged to minimize the potential bias from a single method.

27

### 5.0 PRODUCTIONSEGMENT Some ofthe 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 extrapolationparameters: total gas well count, gas industry well count, and gas productionrate. Any scaled-up site data were extrapolatedusing the ratio ofsite wells to national wells orusing the ratio ofsite productionto national production. 

### 5.1 Development ofa RegionalApproach The extrapolations were performed on a regional basis since regional differences were known to exist and because each ofthe well-known activity factors (i.e., wen count and production throughput) were also known on a regional basis. Six regions were selected based uponananalysisofthe productionand well populationcenters in the United States as well as upon knowndifferences in practices 

### in various regions. Theregions are: GulfCoastOnshore, GulfCoast Offshore, CentralPlains (onshore), Atlantic and Great Lakes (onshore). Pacific and Mountain (onshore), and Pacific Offshore. Figure5-1 shows the majorproducing states and Figure 5-2shows the regions selected and which states are included. Thedifferences in the regionsjustify 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 uniqueoil well versus gas well split and has a unjque production rate perwell. Two offshoreregions exist to account forthe known differences in practices between onshore and offshoreproductionoperations. Thewell and production demographics also support this split since the offshoreregions account for a smallportion of the wells (!.5% ofthe gas wells and !.2% ofthe oil wells) butproduce 27.7% ofthe U.S. marketed gas production.


### 28

# Stateswith Production >50,000 MMscf/yr Figure5-1. MajorProducingStatesw 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 GulfCoast 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 thatare >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 ofGross 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 otherregions, as shown in Table 5-1, the majority ofnarural gas production in the United States (61.5% ofmarketed production) is in the GulfCoast Region. All other regions only account for 38.5% ofthe production, and the majority ofthat fraction ofproduction 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% ofthe marketed national gas production, has the largest portion ofgas wells (46.8% ofthe national total) as well as a large fraction ofoil wells (23%). If

### wells orequipmentassociated only with wells (such as heaters and chemical injectionpumps) are the sources being evaluated, thenbias would potentially be introduced if sampling was performed only on wells in the GulfCoastregion, where most ofthe gas is produced. The bias is avoided ifthe 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 ofthe sample data set for the region that itrepresents. The percentage ofsite samples (site visits and surveys) in the 

### region can be 

### compared with the actual percent ofnational well count and national productionthat the region represents. The sample set does not exactly match the actual regional averages forproductionper well. The sample sites have ahigher productionper well than the 

### actual region, probably due to lower participation ofsites with very low production rates. (Lowerproductionrates may be 

### tied to fewer resources and less ability to participate in surveys.) However, this bias has not caused significanterror in the 

### data set since the extrapolationtechnique for activity factors does not use the production rate per well. Instead, the extrapolation technique uses both well countand productionrate independently. Therefore, the high productionrate 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 perGas Industry Number Per<'ent ofTotal Well (MMcfd)

ofSite SiteSamples Regional Groupings Samples from tbis Region Marketed Gross GulfCoast 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, GasFacrs 1 <Narural Gas Annual 5 1992 Actual Regional Data&,c Production Rate per Gas Industry Percent of Percentof Percentof Well (MMcfd) Total Oil Total Gas Marketed

Wells Wells GasTotal 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 Countand Production (Onshore andOffshore) Gas well counts for 1992 were available from several sources (Gas Facts, Table 

### 3-17,' or from World Oil,6 orfrom DOEEIA's Natural GasAnnual'). These sources give total U.S. gas wells for eachyear. The data are also sorted 

### by state, which allowed regional groupings to be made, as was done in Table 5-1. 

### The split ofcounts into offshore and onshore weiis has to be made for the states that have offshoreproduction 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 theTexas Railroad Commission (RRC).7 The Alaska offshore count was provided by the Alaska Oil and Gas Conservation Cornmission.8 Gas

**w~Il** productiondata for 1992 were also available from several sources (Gas Facts. Table 

### 3-17,2orfrom the U.S. Department ofEnergy (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 offshoreproductionusing DOEEIA'sNatural GasAnnual, Table4, "Offshore Gross Withdrawals ofNatural Gas By State, 

### 1992." 

### 5.3 Oil Well Countand Production (OnshoreandOffshore) Oil well counts for 1992 were available from the Oiland Gas JoumaP and from World Oil(Feb 

### 1993).6 Similarto gas wells, the onshore and offshore split ofoil wells was madeby World Oilfor 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



### 34Gas production from oil wells for 1992 was also available from DOE EIA's Natural GasAnnual 5• 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'sNatural Gas Annual, Table 4, "Offshore Gross Withdrawals ofNatural Gas By State, 1992." 

### 5.4 OilWellsMarketingGas Oil wells that produce natural gas and sell the gas into the natural gas system have been considered apan ofthe natural gas industry for the purposes ofthis report. The wellhead is not considered part ofthe gas industry, but downstream gas production equipment associated with these wells is included. However, oil wells that do not produce gas, orthat produce gas that is vented orconsumed on site are not considered p<irt ofthe gas industry. Methane emissions from this latter type ofoil well are not included in our estimates. There are no national statistics onthe fraction ofoil wells that market gas. Only the total oil well count is known. Even differentiation ofgas 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 lowerthan 100,000cubic feet (ofgas) per barrel (ofoil). A GOR greater than 100,000qualifies 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 ofoil 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 whethergas from the wells was marketed orinternally consumed. Only Texas data allowed such a differentiation.

35

### Texas RRC magnetic tapes for 1989'sP-1 and P-2 disposition repons were purchased, downloaded, and analyzed with adatabase 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% ofall oil well leases. This figure was obtained by taking the mean ofthe percentages for each month's worth ofdata. 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 ofstatistical confidence limits, was 2.8percentagepoints, or 7.8% ofthe mean value. Thereader should note that this error demonstrates the variability ofthe Texas data and has been assumed to be 

### equal to the variability ofthe national data. Dwight's Energy Data, Inc. was asked to query theirdatabase to detennine the fraction ofoil leases that were producing gas for sale in Oklahoma. 11 

### They found that 52% ofthe oil leases in Oklahoma had sold gas. However, Dwight's was unable to 

### confirm what GOR was used to differentiate betweenan oil lease and agas lease. Thus, the 

### 52% was not used 

### in the calculation ofassociated 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 ofthe 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 ofchemical injectionpumps, inline heaters, separators, and compressors as detailed 

### in Section5.7. 

### 5.5 OffshorePlatforms A separate method for detennining offshore platfonncount was necessary because offshore productionfugitive studies produced emissionrates 

### per platform, rather than per equipment. While published data are available on the number ofactive drilling rigs, no published data were found onthe numberofproducingplatforms. Two separate sources were contacted to obtain the required information. 36

### TABLE 5-3. NATIONAL GAS INDUSTRY WELLS
Oil Wells GasWellsb Oil Wells' Marketing Gas• Region (1992 data) (1992data) (1992 data) GulfCoast Offshore 4021 5140 1780 GulfCoast Onshore 59646 212427 73700 Central Plains (onshore) 80924 199103 69100 Pacific Offshore 65 2040 708 Pacific & MountainOn"hore 220l 44682 15500 Atlantic & Great Lakes (onshore) 129157 138805 48200 TOTAL U.S. (l992data) 276014 602197. 209000 • Based upon analysis ofTexas RRC data that shows 34.7% ofall oil wells market gas.7· 10 b Source on well counts: Gas Facts. 2 ± 90% Confidence Limits (absolute) 133 5479 5135

53 I152 3580 15533 

### ± 90% Total Gas Confidence Industry Wells Limits

(1992 data) (%ofMean) 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 offshoreproduction. They estimated that in 1993 there were 1607 producingplatforms in the GulfofMexico, and 45 platforms in the rest ofthe United States. This includes platforms in state waters. The second source forplatform data was the Minerals Management Service MMS OuterContinental ShelfActivity Database (MOAD). 13 

### This database provided a count of 1,857 producing platforms in the GulfofMexico (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 forplatforms located in state waters in the Gulf. The MOAD numbercould beoverestimated 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 GulfofMexico platformcount because they were conservatively high and could be 

### examined more readily. The sum ofthe extrapolated MOAD GulfofMexico count and the Offshore Data Services count for the rest ofthe United States was used inthe study. The total platformcount was split50/50between "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 ofgas 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 perplatform. This ratio validates the relative magnitude ofthe platform countsprovided by Offshore Data Services and the MOAD


### 38TABLE 5-4. PLATFORM COUNTS Total Gas Industry Region PlatformConnt Platform Count Rest ofU.S. 45 22 GulfofMexico 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 

### engineeringjudgement. 

### 5.6 SpecificEquipment Nationally tracked data are not available for mostequipment in the production segment. Therefore, this study estimated total equipmentinthe production segment from specific counts taken during the study's site visits. Atotal equipmentcount for a facility was estimated while on site. Then the site equipmentcounts were extrapolated to regional counts with anextrapolationparameter (something 

### that is known both for the site and for the region). Section4.4, Equation 3 discussed the method used for extrapolation. Formajor equipmentactivity factors 

### in the production segment, equipment counts could be extrapolated by eitheractive wells ormarketed production for each site. As explained in Section4.4.2, the active wells extrapolationparameter canbe either gas only wells ortotal "gas industry"' wells. "'Gas industry"' wells include gas wells and oil wells that also market gas. These three extrapolationmethods (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 Section4.4.2, 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 ofactive wells (gas and total wells) orby regional marketed production to obtain a total count ofequipmentin the region. Regions were added together to determine the national number. The final national equipmentcounts are summarized inTable 5-5. 

### TABLE 5-5. SPECIFICEQUIPMENTACTIVITY FACTORS

ActivityFactor 

### Countand ConfidenceLimits Separators 167.000 ± 28% Heaters 51,000 ± 

### 95% Pneumatics 249,000 ± 48% Chemical Pumps 17,000 ± 143% Compressors 17,100 ±52%
The results ofthe extrapolations for separators, in-line heaters, pneumatics, chemical injectionpumps, and compressors arepresented in Tables 5-{) through 5-10. The last columnin Tables 

### 5-6through 5-10presents the selected activity factor value. The site equipmentcount data used in developing the regional counts ·in Tables 5-6through 5-10 are presented in Appendix A. 

### 5.7 Vessels andPressure ReliefYa!ves Specificequipment activity factors were combined 

### and used to extrapolate emission rate estimates for vessel blowdowns and pressure reliefvalves (PRVs). The number ofproduction "vessels" (used inestimating vessel blowdownemissions) was based onthe sum ofseparators and in-line heater counts from Tables 5-6 and 5-7 and 

### the number ofdehydrators (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. Aconfidence interval of± 26% was determined by 40

### TABLE S-6. SEPARATORS IN PRODUCTION Arith. Mean GasWell Basis MarketedGasBasis (Gasand 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 NATIONWIDEBasis{non-regional) No< 0.727 200,566 4. !82 214.399 Applicable NATIONWIDESum by Region 200,312 26% 134558 36% 167,172 GulfCoast Offshore Region 0.335 1,349 129% 0.060 825 42% +I· GulfCoastOnshoreRegion 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/MountainOffshore Region 0.083 5 300% 0.006 3 101% Pacific/MountainOnshoreRegion 0.849 1,869 300% 0.412 902 268% 0.700 1,385 271% Atlantic/GreatLakes Region 0.766 98,991 17% 38.972 8049 32% 0.859 91.670 23%

### TABLE 5-7. IN-LINE HEATERS IN PRODUCTION

G8sWeU Bam Marketed GasBa!ris Final TotalWeD ActivityFactor Extrapolated 90% Acthityfactor Extrapolllted 90% Extrapolated In-LineHealer$ Basis Equip/Gas Well Count Co»l EqWp/Mkt Gali Count Co»l 

### ' Count NATIONWIDEBasis(non-regional) No< 0.282 77,865 Not Applicable No< NATIONWIDESumby Region Applicable 51,000 95% Applicable 51,000 GulfCoastOffshore Region 0000 0 O% +I· GulfCoastOnshore Region 0.224 13,342 121% 95% Cemral PlainsRegion 0.435 35,!97 128% Pacific/MountainOffshore Region 0.000 0 0% Pacific/MountainOnshoreRegion 1.000 2,201 JOO% Atlantic/Grear LakesRegion 0.002 260 196%

### TABLE 5-8 PNEUMATIC DEVICES IN PRODUCTION 

# . Arith. Mean TotalWeU Basi.oi MarketedGasBasis (Gas and Mkt.) WI. Mean Final ActivityFactor Extrapolated GasWell Activity Factor Extrapolated 90% Equipment 90% Extrapolated PneumaticDevices Equip/Gas Well Count 90%Conf Basis Equip/Mkt Gas Count Conf 

### ' Count Conf Count NATIONWIDEBasis (non-regional) 0.5!1 250.958 3.458 177,253 NATIONWrDESum by Region 353,356 56% 144,867 35% 249.111 4&% 249,11 I GulfCoastOffshore Region 0.127 739 224% No< 0.077 1,049 26% -0. !02 894 92% +I GulfCoast OnshoreRegion 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/MountainOffshore Region 0.000 0 0% 0.000 0 O% 0.000 0 0% Pacific/MountainOnshoreRegion 0.080 1,423 131% 0.666 I,455 249% 0.703 1,439 222% Atlantic/Great LakesRegion 0.427 75,654 54% 23.072 49,937 77% 0.950 62,796 62%

### TABLE 5-9. CHEMICALINJECTION PUMPS IN PRODUCTION Arith. Mean GasWeU Basis MarketedGasBasis (Gas and Mkt.) Wt. Mean Final TotaJ Activity Factor Extrapolated

# .... Activll)' Factor Extrapolated 

# .... Equipment 90% Extrapolated CMmicalInjection Pumps Well Basis Equip/Gas Well Cowrt Coot Equ1p1Mkt Gas Count Cool 

### ' Count Cool Count NATIONWIDEBasis (non-regional) No< 0.036 9,903 Not Applicable NotApplicable NATIONWIDESumby Region Applicable 16,971 !43% !6,97! GulfCoastOffshoreRegion 0.000 0 0% +I· GulfCoastOnshoreRegion 0.033 1,962 142% !43% Central Plains Region 0.159 12,857 184% Pacific/MountainOffshoreRegion 0.000 0 0% Pacific!MountainOnshoreRegion 0.679 1,495 300% Atlantic/GreatLakesRegion 0.005 6S7 48%

### TABLE 5-10. COMPRESSORS (NON-GAS L!Ff)

### IN PRODUCTION Arith. Mean TotalWeU Basis MarketedGasBasis (Gasand Mkt.) WI. Mean Final Compressors Activity Factor Extrapolated GasWeD Aclivlly Factor Extrapolated

# .... Equipment 90% Extrapolated (excluding gas lift) Equip/GasWeU Cowrt 90% Conf Basis Equip/Mkt Gas Count Com 

### ' Count Com Count NATIONWIDEBasis (non-regional) 0.067 32,317 0.!95 9,977 NATIONW!DESumby Region 26,000 59% 8,223 41% 17,111 52% 17,1 !2 GulfCoast Offshore Region 0.000 0 0% Nol 0.000 0 0% 0.000 n 0% u GulfCoast 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/MountainOffshore Region 0.167 129 300% 0.013 6 300% 0.700 68 296% Pacific/MountainOnshoreRegion 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 ofthe sum ofthe squares approach (discussed in Volume 4 on statistical methodology3). An activity factor for the numberofPRVs was based on an observed PRV count perequipment type as shown in Table 5-11.

Separators Heaters Compressors Dehydrators 

### TABLE 5-11. PRESSURE RELIEFVALVES EquipmentType PRY Count PerEquipment 2 

### ± 68% I 

### ± 89% 4 

### ± 84% 

### 2 ±53% Details ofthese observations are provided in Volume 8 on equipment leaks.14 Using the PRV ratios shown above and the equipmentcounts reported in Sections 5 and 8, results in an estimated529,400 

### ± 53% PRVs in production. 

### 5.8 Gatherin&PipelineMil§ Total gatheringpipelinemileage is not reported ortracked nationally and must be estimated. The gathering pipeline mileage was divi<;led into three segments. Thefirst of these was gas well gatheringpipelinemiles, and the second was miles for oil wells that marketgas. The third segment consisted ofgatheringpipeline miles owned by transmission companies. Total miles ofgatheringpipeline were estimated using site visit data from the thirteenproduction sites shownin Table 5-12. Seven ofthe thirteen sites provided estimates oftheirtotal miles ofpipeline. The fifth site'smileage was estimated from a map ofits pipelines. 46Gas 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 ofthe gathering pipeline miles (1,359) was divided by the sum ofthe gas wells (2,033) to determine an average number ofpipeline miles pergas well (0.668). This value was extrapolated by the nationwide activity factor for gas wells (276,000) to give the nationwide activity factor of 184,000gathering pipeline miles for gas wells. Gathering pipeline mileage for oil wells that market gas was based on the gas well mileage data. Itwas assumed that one halfofthe gathering pipeline mileage at an oil weii that markets gas was in the oil industry, and halfwas in the gas industry (see Figure 3-2). Therefore, the average number ofpipeline miles per oil well marketing gas was divided by two to detennine the average number ofpipeline 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 of70,000 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 86,200miles (Table 5-12). Utility-owned pipelines are assumed to be part ofthe 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 of340,200for production gathering pipeline miles. A rigorous determination ofthe 90% confidence interval gave an error less than 

### ± 4%. This value was _considered to be too low based onthe quality ofthe data that were used to generate the activity factor. Thus, a 90% confidence interval of 

### ± 10% was assumed based on engineeringjudgement.



### 47

### TABLE 5-12. PRODUCTIONGATHERING PIPELINE MILEAGE- GAS WELLS Gathering Site Miles NumberofWells 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 MaintenanceActivities Emissionrate estimates for well workovers and completionflaring were based 

### on annual counts ofa specific activity, as opposed to anequipment 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 withexploratory wells toallow proper sizing ofmeters and surface equipment. 

### (Theproduction rates for new wells inexisting fields canusually be detennined before the well is completed.) The EnergyInformation Agency (EIA)"estimated 844 wells are
48

### completed each year, based on 1987 data. Aconfidence interval of 10% was assigned to this value based on engineeringjudgement. Well workovers arc another type ofmaintenance 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 numberofgas well workovers for two sites. 16 

### PSI data showed I workover/yr per 21 gas wells at Site 1, and 1workover/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 ofwell workovers per year: 276,014 gas wells X0.0338 workovers/yr-well = 

### 9,329 workovers/yr A confidence interval of258% was calculated based on propagating the confidence intervals for the terms in the equation above. A third type ofmaintenance, venting or gas well unloading, requires an activity factor for the number ofgas wells that require unloading. Gas wells operated under low pressures can accumulate water in the wellbore due to their low flow rate. This water chokes the flow ofthe well, reducing the gas production. To clearthe 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%) ofgas wells require this unloading practice to maintainproduction. Additional details on this particular activity factor can be found in the Volume 7 on blow and purge activitiesY Multiplying the total number ofgas 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 extrapolationprocedure for all segments ofthe natural gas industry. The details ofthis procedure are discussed in Section 8.



### 50

### 6.0 GAS PROCESSING SEGMENT Activity factors in gas processing are significantly simplerthan in gas production, since the segment consists ofone type offacility: gas processing plants. Major 

### activity factors were limited to the numberofgas plants (ofeach type), dehydrators, acid gas recovery units (AGRs) and compressors. Ail ofthese activity factors were eitherpublished 

### and weii-defined or were developed throughother studies such as the Wright Kiiien report18 orVolume 11 oncompressor driver exhaust. 19 

### 6.1 GasPlantCount Gas processing plants recoverhigh-value hydrocarbon liquids from natural gas. Theplants canbe 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 OFGAS PROCESSING PLANTS BYTYPE Type Count %ofTotal Cryogenic 302 42 Refrigerated absorption 117 16 Refrigeration 192 26 Other 115 16 TOTAL PLANTS 726 100
Since the sites visited sampledall ofthe major types, and since no emission factor data were correlated toplant type. only the total plantcount was used for the plant activity factor. Based upon !992 data from the 

### Oiland Gas Journal, the total gas plant count is 726. A confidence limit for the gasplant count was set at 

### ± 2% based upon engineeringjudgement.

51

### 6.2 Acid Gas Recovery (AGR) Units Acid gasrecovery (AGR) units are used to remove carbon dioxide (C02 ) and 

### hydrogen sulfide (H2

### S) from natural gas. Absorption ofC02 

### and H 2 

### S 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 byheating and decrease in pressure. Purvin & Gertz, Inc., estimated the total countofAGRs using anamine process to be 371, 

### with a total capacity of

### 30,433 MMscfd.20 Most AGRs have flares or sulfurrecovery processes that prevent the release ofmethane. However, it is estimated that only 18% ofAGRs vent regenerator offgas (including acid gas and methane) to the atmosphere.21 OtherAGR processes may result in methane emissions but are much more difficult to quantify because of the wide variety ofprocesses available. 

### 6.3 Compressors andDehydrators Activity factors forcompressors and dehydrators were calculated in a single extrapolation procedure for all segments ofthe natural gas industry. Thedetails ofthis procedure are outlined inSection 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. Thetransmission segment has only four basic components: pipeline, compressor stations, storage facilities, and meter and regulating (M&R) stations. Most transmission pipelines are one oftwo types: cross-country or regional network. 

### 7.1 Compressor Station Count The total number ofcompressor 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 ofFERC Form2 list the number ofcompressor stations and miles ofpipeline for 46transmission companies. The pipelines owned by these companies comprise 70% ofthe total transmission pipeline mileage, making the sample large but incomplete. The total number oftransmission compression stations reported in the forms was extrapolated by pipeline mileage to determine the total number ofcompression stations in the transmission segment. A summary ofthe compression station data obtained from FERC Form 2 is presented in Table 7-1. The confidence interval of 

### ± 10% was based onengineeringjudgement. 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 ofmiles per stationcorresponds 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 oftransmission 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 ofStations Miles ofPipeline 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 ofStations Miles ofPipeline 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. ofStations 1,700

55

### 7.2

**Stora~e** StationCount There are two types ofgas storage facilities: liquefied natural gas (LNG) storage and underground storage. LNG facilities condense the natural gas athighpressures for storage and transportation inrefrigerated vessels. Underground gas storage areas typically operate to accept excess gas from majorgas pipelinesduring surplusproduction flow periods. The stored surplus gas is reinjected into the pipeline when demand is higher. Typically, the net amount ofgas to storage for any year is

**zero~** 

### ifthere is an actual longterm over-supply. the producing wells will simply 

### be cutback. Underground storage areas areunique. Unlike liquid storage facilities, expensive tanks are not used. Underground gas storage facilities are typically one ofthe following: old, spent gas fields; saltdomes; oraquifers. 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 orto return the gas to the pipeline. Underground (UG) stationcountdata were available from Gas

### Facts, Table4-5 "AmountofPools, Wells. and Horsepower in Underground Storage Fields. By State. 1990-1992."2 Thenumber ofLNG facilities was alsoavailable from Gas

### Facts Table 4-3, "LNG Storage Operations 

### in the U.S. as ofDec. 31. 1987."24 The total numberof storage stations is the sumoftheunderground stations and LNG facilities as shown: UG: 386 (storage stations) LNG: 

### + 89 (54 completeplants, 32 satellite plants. 3 import tenninals) TOTAL: 475 (storage stations) This project assigned a ±5% accuracy based upon engineeringjudgement. 

### 56

### 7.3 StoraeeWell Count Underground storage facilities have wellheads at the surface similarto wellheads at producing gas wells. These wellheads sit above the wellbore's connection to the underground reservoir. Each wellhead has components that canemit gas. The number ofactive 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 StationCount Meter and regulating (M&R) stations are used to measure flow ofgas at a custody transfer point, and/or to reduce andregulate the pressure and flow into a downstream pipeline system. Somemetering facilities do exist withincompressor stations, butthese have already been counted in the compressor station emissions. Inthe transmission segment, separate M&R facilities are usually fenced, above ground facilities that contain valves, piping, and meterruns. Inthe U.S. gas industry, most M&R stations existin the distribution and production segments and have already been counted there. M&Rfacilities counted by the transmission companies are generally ofthe following type: 1. Receipt from production (meteronly), 

### 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 otheractivity factors as follows: • In the production segment, meter runs that transfer the gas to transmission companies were included in the well sitefugitive component countdata. • Deliveryto 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 thedistribution M&R count (Section 9).
The two types ofM&R stations that

### have not been included elsewhere are transmission company interconnects and direct sales. Most large transmission companies have interconnects with othertransmission companies to 

### allow for flexibility ofsupply. These shared stations can flow in either direction. The last category (direct sales) is comprised oftwo types: farm taps and direct industrial sales. Direct industrial sales from transmission lines are owned by local distributioncompanies (LDCs), even ifthey only own a few feet ofline. Many fann taps are still owned by transmission companies, even though there is a trend to 

### let LDCs handle the 

### farm taps orto remove them entirely. Transfers to other transmission companies and fann taps were calculated from survey data provided by the meteringdepartments ofthree large (over 10,000miles of pipeline) transmission companies, and from three companies with fewer than 10,000miles of pipeline, as shown in Table 7-2.



### 58TABLE 7-2. TRAJ'ISMISSION M&R STATION POPULATIONS Transfer to

another Direct Company Transmission Co. Farm Taps Industrial Sales Miles ofPipeline 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 ofthe six companies that responded to the survey reported having interconnects with othertransmission companies. The activity factor was extrapolated based onpipeline miles and was calculated to be 2533 interconnects (transfers). The 90% confidence bound was determined to be 

### ± 776%. The count offarm 

### 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 onlya small percentage ofall transmission companies have these M&R stations, yet two ofthe six companies in our data set reported a large number offarm taps. The activity factor for directindustrial sales was developed from FERC Form No. 2, page 306.22 Industrial sales greater than 50,000Mcfare listed individually, while sales less than 50,000Mcfare combined into a single item. In the latter case, the total amount ofgas sold was divided by 25,000toprovide anestimate ofthe number ofsales. The national activity factor was extrapolated from the FERC data to give a total count of938 direct industrial sales. Due to the uncertainty that this approach introduced to the activity 59

### factor and to 

### the complexity ofretrieving data from FERC, a confidence bound of ± 100% was assigned based onengineeringjudgement. 

### 7.5 Miles ofPipeline The total miles oftransmission pipeline equals 284,500miles for 19922

### and is available from 

### Gas Facts, Table 5·1. A confidence interval of± 5% was assigned based on engineeringjudgement. 

### 7.6 Compressors andDehydrators Activity factors for compressors and dehydrators were calculated in a single extrapolation procedure for all segments ofthe natural gas industry. The details ofthis procedure are outlined in Section 8. 

### 7.7 Pneumatics The number ofnatural gas operated pneumatic devices in

### the transmission and storage segment was calculated based on the average numberofdevices perstation multiplied by the total number oftransmission and storage stations nationally. Data for determining the average number ofpneumatic devices per station are presented in Volume 12 on pneumatic devices.25 

### The total number oftransmission and storage stations is the sum of 1,700 transmission compressor stations, 386underground storage facilities, and 89liquid natural 

### gas storage facilities (2,!75).

60

### 8.0 CROSS-SEGMENTACTIVITY FACTORS Many equipment types are u:1ique to a particular segment ofthe natural gas 

### industry. The activity factors from these types ofequipment, 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, afew equipment types are common to all segments ofthe gas industry. Activity factors for these equipment types were determined in one step for the entire industry. This section ofthe report discusses two equipment types, compressors and dehydrators. that were determined on an industry-wide basis. 

### 8.1 

### Compressors Activity factors were developed for compressorhorsepower-hours and for compressor counts on an industry-wide basis. Compressorhorsepower was used as the basis for the compressor exhaustcalculations, and compressor counts were used as the basis for the fugitive and unsteady emission calculations. Compressors exist in all segments ofthe industry except distribution. 

### 8.1.1 Compressor Horsepower-Hours The two main types ofcompressor drivers in the industry are reciprocating gas engines and gas-fired turbines. Twopieces ofinformationare needed to calculate the activity factors, expressed as horsepower-hours (hp-hr), for the two types ofdrivers in each segment ofthe industry except production. These data are the installed horsepower and the average annual operatinghours. Forthe 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 Installed Annual Annual Engine Turbine Hours Hours Engine Turbine Industry Segment MMhp' MMbp• Engine Turbine MMHpiJr MMHp·hr Production NA 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%

**~** Does not include horsepower associated with gas lift for oil recovery or with electric drivers. b Average oftwo estimation methods.

### To determine the horsepower and operating hours for engines and turbines, four major sources ofdata 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) FormNo. 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/SwRIdatabases 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 briefdiscuss-ion ofthe development ofeach ofthe activity factors foliows. (Forfurtherdetails, refer to the report Methane Emissions

**fr~m** 

### theNatural Gas Industry. Volume 11: CompressorDriver 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•hrusing 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 shownby 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 insignificantand was therefore not included in the summary 

### 63calculations. This emission source would be an insignificant source due to the small number ofthese drivers in this industry·segment and the low methane emission factor for turbines. Processing--The p:.ocessing segmenthorsepower was detennined by extrapolating site visit data for tengas plants. The average oftwo extrapolationmethods was

**use~.** The first method extrapolates the site data to a national numberby multiplying the total U.S. gas plantthroughput as ofJanuary 1, 1993 (46,510.7MMcfd, Oil & 

### Gas Joumaf) by the site visit horsepower perthroughput (47.8 hp/MMcfd forengines and 59.2 hp/MMcfd for turbines). This gives a total of2.22MMhp for engines and 2.75 MMhp for turbines. The second method scales up the data by plant count, assuming the visited plants areperfectly representative. Anational estimate is produced by multiplyingthe horsepower from the site visits by aratio of726/10 [the726gas plants in the United States (Oil & 

### GasJouma{) to the 10 sites visited]. This second method, therefore, uses a scaling factor ofapproximately 73 for processing horsepower and assumes that all gas plants have approximately the same throughput, despite the variation found in the site data (40MMcfd to 750MMcfd). The activity factors based onthe number ofgas plants are 6.16MMhpfor engines and 7.62MMhp for turbines. Toprovide a more coruervative estimate, the two methods were averaged whencalculating the national estimatefor processing horsepower. Theresulting activity factors are 4.19MMhp forengines and 5.18MMhpfor turbines. The annual operating hours were based on the ten site visits and dataprovided from two companies (18 additional sites). Foreight ofthe e!tes visited, typical operating hours for each ofthe compressors were not available. Therefore, all compressors that were running during these site visits were assigned annual operating hours of8760 and an compressors that were idle were assigned annual operating hours of0. Anaverage ofthe average operating hours per site was calculated to get the processing segmentoperating hours 

### 64(203 engines and 9 turbines). Confidence limits were calculated for the horsepower and operating hours estimates from the variation ofthe data. Transmission·· Transmissionlosses from compressor exhaust have been divided into two subsections: compressor stations and storage fields. Foreach ofthese 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 ofGRI TRANSDAT (11.2 MMhp for engines and 5.0MMhp for turbines). This horsepower number accounts for about97% ofthe gas utility industry installed 16.7 MMhp reported by A.G.A. for 1989.23 The horsepower values 

### in GRI TRANSDAT are a combination oftransmission and storage compressordrivers. GRI TRANSDAT also reports that there were 1.5 MMhp in the storage segment in !989. Since GRI TRANSDATdid not give separate values for engines and turbines in storage, the ratio ofengines 

### to turbines in 

### the overall database (11.2:5.0) was used giving 1.0MMhp 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 10.2MMhp forengines and 4.5 MMhp for turbines in the transmission segments. Confidence limits for horsepowercould not be rigorously calculated from the GRI TRANSDAT database because the installOO horsepower was given by installation, or site, and then summed tocalculate a national estimate. Therefore, confidence limits were set based on engineeringjudgement.



### 65The annual operatinghours are based on data reported on FERCFortn No. 2 for the year 1992.22 The FERCdatabase did not identify the type ofdriver (i.e., reciprocating engine orturbine). As aresult, additional data from GRJ TRANSDATand one transmission company were used to split the FERChours between the two driver types. 

### Confidence limits were calculated for the operating hours estimate from the variation ofthe l'ERCdata. Transmission StorageFields--The storage segmenthorsepower (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% forengines and 30.9% for turbines). The horsepowerdata were gathered by A.G.A. through a survey oftheir membercompanies. whoseproductionmakes up approximately 96% oftotal gas industry sales. Confidence limits forhorsepowerdata were calculated based on an assumed 

### ± 5% uncertainty forthe A.G.A. horsepower and an assumed 

### ± 10% uncertainty for the GRJ TRANSDAThorsepower splits. The annual operatinghours are based on site visit data and a company database for a total ofeleven storage stations (50engines and 6 turbines). Confidence limits were calculated for the operatinghours estimatesfrom the variation ofthe data. Transmission Generators--Generators were found atboth compressor stations and storage fields. The generatorhorsepower for compressor stations is based onthe total installed horsepower at seven sites and data provided by one company for34compressor stations (35,006 hp forengines and 1080hp for turbines). To extrapolate to a national estimate, the horsepowerperstation was multipliedbythetotal numberoftransmission compressor stations in the United States (1700, FERC Fortn No. 2).22 Thegenerator horsepower for storage fields is based onthe total installed horsepower for one company with nine storagefields (3 engines and 1turbine). To
66extrapolate to a national estimate, the horsepower per field was multiplied by the total number ofstorage fields in the U.S. (475; A.G.A. 

### Gas Facts, Section 7.2 2). Generator annual operating hours were also provided by the same sources providing horsepower data. An average ofthe average generator operating hours per site was calculated to determine generator operating hours for compressor stations (87 engines and 1turbine) and storage fields (2 engines and 1turbine). Confidence limits were calculated for the horsepower and operatinghourestimates from the variation ofthe site/company data except for the case ofgenerator 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), inhorsepower-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 termsusing a standard statistical approach. 

### 8.1.2 Compressor Counts Compressor counts were developed using a variety ofsources. National data from DOE and site visit data were used to make the estimates. Table 8-2 provides a summary ofthe 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 indetail in Section 5.7. Confidence bounds were calculated for these estimates from the variation ofthe site data.

67TABLE 8-2- COMPRESSOR COUNTS INTHEGAS INDUSTRY Reciprocating Total Segment Engines Turbines Compressors Production 17,112 ± 52%b 0' 32,327 

### ± 64% Gas Plants 4,092 

### ±47.7% 726 

### ± 76.6% 4,818 ± 44% Transmission 6,785 

### ± 16.6% 681 ± 16.2% 7,466 ± 15% Storage 930 

### ± 58.3% 136± 119% 1,066 ±54% Combined Transmission & Storage 7,715 

### ± 10% 817 ± 10% 8,532 

### ± 9.1% Total Industry 44,134 1,543 45,677

Only one production site (out of27 with data on compressors) had any turbines. However, those "gathering compressors" actually were physically part ofan attached gas plant, and were therefore added into the gas plant counts. b Non-gas lift compressors Along with the count oftotal 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 ofthe lack ofagood seal onthe blowdownvalves, these blowdown lines were found to have extremely large continuous fugitive emissionrates. These emissionrates had not been considered or measured until the GRI/EPAcampaign 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, orthat the vent lines were small and located proximate to the compressors so that the API/EPAfugitive measurement efforts had included them. However, several companies reported that some large gathering compressor stations (thathad a large number ofengines) were similar to transmission stations, and did have these separate vent lines to a separate stack. Therefore, forproduction, the FERC22 data were analyzed for gathering stations. (Gathering was included in the production segment.) InFERC Fonn No. 2, the number ofstages ofcompression operating on the peak demand day is reported. It was assumed that there are an average of3.3 stages ofcompression per compressor, and that a 

### 68large station would contain at least five compressors. Therefore, a station with 16 ormore stages ofcompression was counted as a large gathering compressor station. Based onthis criteria, three large gathering compressor stations were identified in the FERC data. This number was thenextrapolated from the 86,200miles ofgathering pipeline mileage owned by transmission companies, to the national activity factor of340,000gathering pipeline miles, for a total oftwelve large compressor stations in production. The FERC data also showed that the large compressor stations averaged 26 stages ofcompression, oreight compressors per large station. This value was extrapolated in the same manner to provide a national activity factor of96 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 onengineeringjudgement. Processing-The compressorcounts forthe processing segment were determined from eleven gas plant site vi$its. The average ratio ofcompressors perplant 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 ofthe site data. Transmission & Storage--TheGRI TRANSDATindustry database was used to estimate the total compressor counts." The industry database of 16.2MMHp bad 7489 engines and 793 turbines. However, the database is notquite a complete sample ofthe transmission/storage sector since its horsepower is slightly lower than the total transmission/storage horsepower of 16.7MMHp 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 ratioofthe two values (16.7/!6.2)to determine the adjusted totals of7715 enginesand 817 turbines. Confidence bounds were not calculated but were estimated to be 

### ± 10% based upon the large size ofthe TRANSDAT database and the tightconfidence ofthe Gas Facts data. The combined transmission/storage count ofstorage 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 of0.750turbines per site(± 119%), 5.13 reciprocating enginesper 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 horsepowerperunderground storage site for 1992 was only 4975 HP/site (Table 4-5, 

### "AmountofPools, Wells, Stored Gas, and Horsepower in Underground Storage Fields, By State, 1990-1992"2 ). 

### Therefore, the siteratios were adjusted to account for the higher than average horsepowerby multiplyingby 4975/13033. The adjusted ratios were thenmultiplied 

### by the 475 storage compressor stations (see Section7.2). Transmission compressorcounts were then determined by subtractingthe countofcompressors in storage ( 1 066) from those determined by the TRANSDATdatabase analysis. 8.2 Dehydrators 

### Multiple activity factors associated 

### withdehydrators were determined for the 

### entire gas industry. These factors are based on industry segmentgas throughput, and the percentage ofunits 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 DehydratorCount A national count of41,700dehydrators 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 DepartmentofEnvironmental Quality (LDEQ)] to determine the count ofdehydrators in the natural gas industry. The Wright Killen study divided thedehydrators into segments ofthe natural gas industrybased 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
70dehydrators in the different industry segments. These values canbe found in the first column ofTable 8-3. TABLE 8-3. U.S. GAS INDUSTRY DEHYDRATOR COUNTS Wright-Killen TotalDehydrator Counts Glycol DehydratorCount Segment (Alltypes) 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/EPA study used the Wright Killen report as the basis forthe overall count ofdehydrators because the report provides a reasonably accurate estimate ofthe industry. The Wright Killen report estimates that there are 7923 dehydrators at 742 plants, or 10.6dehydrators 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 informationfrom 11 gas plants that shows only 1.41 dehydrators per plant. Starting with the 

### Oil & 

### GasJournal annual gas processing plant list, an estimate of the count ofglycol-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 ofglycol-based units in the industry is estimated as 48.6% (a total of498 glycol dehydrators). Similarly, the Wright Killen estimate produces 8507 dehydrators for 2190 transmission/storage compressor stations, or 3.9dehydrators percompressor station. GRI/EPA'sdata show 2 dehydrators for 17 mainlinetransmission 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 smalldehydrators and too many large dehydrators to 71

### represent the true national production totals. This assessment ofthe data is validated by the fact that Texas, where the study was performed, has a higherproduction rate per well than the national average. Therefore, the splits between industry segments may be biased ifthe national percentage ofproduction dehydrators is larger than in Texas. This GRI/EPA studyalso corrected the production total to 37,824 units to maintainthe total national count estimated by Wright Killen. These corrected industry segment counts are listed in the second data column ofTable 8-3. Byusing the site visitratios for 1700mainline transmission stations, an estimate of201 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 Killenstudy also found that onan industry-wide basis, 95% ofthe dehydrators were glycol-using (as opposed to molecularsieve adsorbers or othertypes of waterremoval). Based on the 95% value. the total numberofglycol 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 DehydratorThroughpot Emissions from dehydrators are proportional to the gas throughput ofthe dehydrator. Therefore, an activity factor for throughput for each industry segment was determined. There is adirect relationship between the estimated count ofdehydrators and the average throughputperdehydrator. In fact, the WrightKillen study determined the productioncount by dividing knowngas productionrates by anestimated dehydrator throughput. Therefore, the GRI/EPA study used theWright Killenreport as the basis for the
72dehydrator gas throughput used in this project. The throughput numbers selected for this project are shown in Table 8-4. TABLE 8-4- INDUSTRY SEGMENTGLYCOL DEHYDRATORTHROUGHPUT Average Throughput with 90% Segment ConfidenceIntervals Basis Production 12.4 Tscf/yr ± 62% WK&C (Adjusted Wyoming data) 1 a Gas Processing Plants Transmission 8.63 Tscf/yr 

### ± 22% Oiland Gas Joumaf 1.09Tscf/yr 

### ± 144% TMOGA26 Storage 2.00 Tscf/yr 

### ± 25% A.G.A. GasFactsz


### Production··WrightKilienestimated that the average throughput per dehydrator in production is 2 MMscfd. This value has been validated by analysis ofthree data sources, as follows:

• TheWyomingdehydratorsurvey (as adjusted by Wright Killen), which is believed to

### be the mostcomprehensive survey, reports a value of2 MMscfd per productionsegment 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 productionrate per weii in the Texas region. Therefore, the median value (as opposed to the average value) ofthe Texas survey is likely to be more representative ofa true national average, since the median value is not heavily weighted by a few large dehydrators. The median value ofthe Texas (TMOGA) survey is 2 MMscfd. • The site visits produced an average of 12.5 MMscfd/dehydrator. However, many ofthe sites visited have higherproduction rate-tonumberofwells ratios than the naticnal average. Ifthe two largest dehydrators, which are 

### in the GulfCoast, are excluded from the total, the resulting average is only 1.14MMscfd/dehydrator.



### 73

### The total production throughput was calculated by multiplying the average throughputperdehydratorby the number ofdehydrators in the production segment and by a capacity utilization factor. Operators ofseveral production sites studied for anotherGRI project indicated that the typical production dehydrator was using less than halfits original capacity. Thecapacityutilization factor was determined to be 0.45 

### ± 33%. GasProcessing--Throughput for the gas processing industry segment was determined from the annual OilandGasJournal 

### survey ofprocessing plants.9 

### It was assumed that

### plantsusinga refrigerated process have glycol dehydrationand plantsusing a cryogenic process have some type 

### ofdry-bed dehydration with no methane emissions. Fora total gasprocessing throughput of 17.44Tscf/year, these assumptions lead to a flow of8.63 Tscf/yearthat is dehydrated by glycol. Nodistinction was made between triethylene glycol (TEG) and ethylene glycol (EG), since the methane emissions for the two glycol processes are similar. [Notethat benzene, toluene, ethyl benzene, and xylene (BTEX) emissions for EG may be several orders ofmagnitude lowerthan for TEG.] Transmission-Transmission segment throughput was determined in much the sameway as was production. TheWrightKillen survey indicated an average transmission dehydrator throughput of 14.8MMscfd and an adjusted countof201 transmission dehydrators, for a total throughput ofl.09Tscf/year. Storage--Storage segmentthroughput was determined from data inA.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 dehydratorthroughput was estimated to be 

### 2.00Tscf/year 

### ± 25%.



### 74

### 8.2.3 DehydratorFlash Drums One ofthe dehydrator equipmentaccessories 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 ofthe 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 ofthe TMOGA database does show a relation between dehydrator size 

### and the existence ofa fla-;h drum {especially for sizes below 2 MMscfd), the numberuflarge 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 ofbias as the TMOGA data set. The result was a 3.31% number for production dehydrators. 8.2.4 Dehydrator Gas Pumps Another important piece ofglycol dehydrator equipment is the type ofpump 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 ofdehydrators 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 ofpumps per dehydrator. The results are displayed in the Table 8-6. 

### 8.2.5 Dehydrator Stripping Gas Use The percent ofdehydrators using stripping gas was determined from the site surveys. The site surveys showed that less than 0.5% ofall production dehydrators use stripping gas. The use ofstripping 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 ofDehydrators with Flash

**Drwn~** (with 90% confidencebounds) Unadjusted



### TMOGA Site Selected Industry Segment Survey Results Visits Numbers Basis Production 42.4 ± 8.5% 3.31 ± 43.2% 26.5 

### ± 8.35% TMOGA and site visits Gas 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 ofdehydrators were divided. by survey: Unadjusted TMOGA results (618 in production, 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. SITESURVEY GAS PUMPDATA ActiveGasPumps per TotalIndustry Gas-driven Pump Dehydrator Throughput Throughput Segment (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 DehydratorVaporRecovery 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 ofthese systems prevent the release ofthe methane since methane emissions are not regulated. Because ofthe lack ofprevention 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 gasprocessing plants 

### with vapor recovery systems that consume methane. However, there are likely to be some plants with 

### methane vapor 

### 76

### recovery. Forcalculation purposes, the fraction ofplants 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 ofDehydrators with ActiveStripping Gas (with 90% confidence limits) 0.0047 

### ± 116% 0.111 ± 186% 0.074 ± 118% 0.080 + 118% Fraction ofDehydrators 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 duringthe site visitsbut it is Iikdy that some processing plantshave

flares. A value of0.10 

### ± 90% was used in the calculations.

77

### 9.0 DISTRffiUTION SEGMENT Activity factors weredeveloped for eachofthe sources ofmethane emissions from the distribution segment ofihe 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 ofmains and services used to detenninelosses from pipeline third-party damage (i.e., dig-ins) as well as pipeline blow and purge formaintenance/installation activities. A discussion ofthe methodologyused to derive these activity factors is presented in this section. 

### 9.1 DistributionAnnualEquivalent

### Leaks Since the emission factor for quantifyingemissions from underground distribution mains and services was stratified by pipeuse (mains versus services) andby pipe material (i.e., cast iron, cathodicallyprotected steel, unprotected steel, plastic, and copper), the activity factor was also stratified to extrapolateemissions. With 

### the exception ofcast ironmainpipeline, the activity factorused to extrapolate the leakage estimate forunderground disa:ibutionmains and services was the number ofannual equivalent leaks. (Forcast ironpipeline, the activity factor was the total mileage ofcast iron mains inthe United States, which is a nationally tracked statistic.28) Annual equivalentleaks are defined as the number ofleaks that leak continuouslyyearround. Forexample, ifleaks that are repaired during theyear are leaking forhalfthe year, on average, then eachrepaired leak would 

### be counted as halfan annual equivalentleak. The number ofannual equivalentleaks wasderived from the national database ofleak repairrecords broken downbymains and services (U.S. Deparonentof Transportation, Research and Special Programs Administration).28 To allocate leak repairs intopipe material categories, data were collected from ten local distribution companies


### 78representing different regions within North America. An estimate ofthe national leak repairs allocated by pipe material type is shown in Table 9-1. Toderive arumal equivalent leaks from the national leak repair records, additional information was needed including the number ofleaks found during the year (leak indications) and the unrepaired leaks at the beginning ofthe 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 numberofleak repairs, number ofleak indications, and outstanding leaks at the beginning ofthe 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 ofthe total mileage ofmains and number ofservices by pipe material. Two additional companies provided data without a breakdown by pipe material oruse. (The leak data disaggregated by pipe use and material type are difficult to obtain from many companies, since leak records are oftennot maintained in this manner.) An estimate ofthe total annual equivalent leaks for each ofthe six companies was developed for each pipe material category except cast iron, based onthe following formula:

where 

### TEL OL TEL 

### = OL 

### + Ll 

### + UDL 

### + URL
- (0.5 

### x RL) Total annual equivdent leaks; Outstanding leaks at the beginning ofthe year;

79

### TABLE 9-1. NATIONAL LEAK REPAIRS ALLOCATED BY PIPE MATERIAL CATEGORY Estimated Pre<:ision Average Leak National Extrapolated Percent Leak TotalLeak (Leak Pipe Use PipeMaterial Repairs/Mile" Miles/Servicesb LeakRepairs Repairs Repairs Repairs) Mains Cast Iron 1.38 55,288 76,353 34 69,750 39,957 Protected Steel 0.08 451,466 34,943 16 31,922 14,006 Unprotected 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 42 181,752 206,405 Unprotected 0.027 5,446,393 148,686 50 214,156 190,569 Steel Plastic 0.001 17,681,238 22,986 8 33,107 26,238 Copper 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 nationallytracked database, U.S. Department ofTranspottation, 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 ofthe network not survey during the 

### current year; and Repaired leaks
-- estimated to be leaking halfthe year, on average. Undetected leaks that cannot be found using an industry standard survey procedure were quantified based on informationprovided 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 ionizationdetector (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 FIDinstrument, registering any concentration above background, is investigated using barholing procedures.) Therefore, the numberof undetected leaks is estimated by:

### UDL = 

### [(1/0.85)- 1] 

### x U 

### The total annual equivalent leaks are derivedusing the estimated leakduration for each type ofleak, based on the following:

• Repaired leaks are assumed to be leaking halfthe year, on average. • Outstanding leaks, leak indications, and undetected leaks are estimated to be leaking the entireyear (i.e., 8760 hours peryear). The leak duration ofunreported leaks is factored into the estimation methodology for these leaks. Unreported leaks are those leaks that 

### occur in 

### parts ofthe network not surveyed during the year (i.e., multi-yearsurvey cycle). The number.ofunreported leaks is based on 

### 81the annual leak indications and the undetected leaks as well as the frequency ofthe leak survey. The number ofunreported leaks in the system that is surveyed every n years is calculated based onthe following:

• Forthe first year in the cycle
-- 1/n X (LI 

### + UDL) leaks are leaking halfthe year; (n-l)in X (Ll 

### + UDL) leaks are not yet leaking. • Forthe second year in the cycle
-- lin x (LI 

### + UDL) leaks are leaking the erttire year; lin X (Ll 

### + UDL) leaks are leaking halfthe year; and (n-2)in X (Ll 

## + UDL) leaks are not yet leaking. • Forthe third year in the cycle
-- 2in X (Ll 

### + UDL) leaks are leaking the entireyear; lin X (Ll 

### + UDL) leaks areleaking halfthe year; and (n-3)in x (Ll 

### + UDL) leaks are not yet leaking. • Forthe fourth year 

### inthe cycle
-- 3in X (Ll 

### + UDL) leaks are leaking the entire year; lin X (Ll 

### + UDL) leaks are leaking halfthe year; and (n-4)in X (Ll 

### + UDL) leaks are not yet leaking. Based on the methodologydescribed above, the number ofequivalent leaks was determined for each ofthe sixcompaniesproviding detailed data. Theratio ofequivalent leaks to leak repairs was then calculated for each ofthe companies. The average ratio ofequivalentleaks to leak repairs was used toextrapolate the national database ofleak repairs. Table 9-2 presents a summary ofthe leak record data provided by the six companies, the estimated equivalent leaks, and the corresponding ratio ofequivalent leaks to leakrepairs. As shown, the average ratio ofequivalentleaks to leak repairs is 2.14. The national number ofannual equivalent leaks, brokendownby pipe use and material type. is shown in Table 9-3. As shown, the activity factor for cast iron mains is miles ofpipeline, to correspond totheemission factor in units ofscf/mile-year. The estimate ofannual equivalentleaks is highest for unprotected steel services, followed by protected steel services. Formains, unprotected steel is the category with the highest estimated annual equivalentleaks. The precision ofthe number is based onthe variability in leakrepairdata allocated by material type from tencompanies and the variability in the ratio ofequivalent leaksperleakrepair from six companies. 

### 82co w 

### TABLE 9-2. SUMMARY OFLEAK RECORD DATA FROM PARTICIPATING COMPANIES 

### :':

**~\:n"iuJlif**Le3':o- ' ',•, ,',.,' / ......

..
.. _Aftilual_.Outst.aDdihj; ·· 'EstltnatedTof.ll / 

### •'Ratio.

# orl;qnlv8ient-I,.eaiks COmpllliy 

### ·'· ·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 Mainsonly. b Data not available.

### TABLE 9-3- SUMMARY OFACTIVITY FACTORS FOR DISTRIBUTION UNDERGROUND PIPELINES Average Activity Precision ofActivity EstimatedTotal Factor Factor (equivalent Pipe Use MaterialCategory LeakRepairs" (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 sixcompanies (see Tables 9-l and 9-2). h Based on ratioofannual equivalent leaks to leak repairs of2.14 (see Table 9-2). ' Miles.

84

### 9.2 DistributionMetering andPressure 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 ofemissions from stations in each segment ofthe gas industry. Fordistribution stations, a questionnaire was sent to distribution companies participating in the underground leakmeasurement program and the companiesparticipating in the metering/pressure regulating station measurement program. A total often companies provided demographic informationonthe stations intheirdistribution network. The station countsprovided 

### by the companies were disaggregated by station type and inlet pressure range. Table 9-4presents the demographic informationprovided by the respective companies. Toextrapolate the individual company data to a national numberofstations, a knownindustry statistic was used. The individual company demographic data were combined with the miles ofmainand total gas throughput for eachcompany to detenninethe number ofstations ineach category on aper mile ofmain and perunit ofthroughput basis. Linear regression analyses were performed on the numberofstations versus both miles of mains and total gas throughput. respectively. It was concluded that the number ofstations should be extrapolated by miles ofmains sincethe variability within the sample set was lowerfor number ofstations permile than for number ofstations perthroughput. The average number ofstations permile was calculated from the individual company data and multipliedby the total mileage ofmain pipeline in the United States.28 The standard deviation ofeach number was calculated based on the variability inthe individual company data.

85

### TABLE 9-4. ACTIVITY DATA PROVIDED BY INDIVIDUAL COMPANIES StationType Inlet Pressure Comp Comp Comp. Comp. Comp. Comp. Comp. Comp. Comp. Comp. Comp. Cornp Range (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 •Notconsideredrepresentativeofnationalaverage.Data were collected from three ofthe ten companies on the number of regulating stations in vaults versus above ground. Based on the data collected, about halfof the regulating stations with an inlet pressure greater than 300psig were observed to be located in vaults. Forregulating stations with an inlet pressure between 40 and 300psig, the majority ofstations in urban areas were in vaults and the majority in rural areas were above ground. On average, it was estimated that55% ofthe stations are located in vaults with an inlet pressure between 100 and 300 psig. Forregulating stations with an inlet pressure between40and 100psig, about 70% ofthe stations are located in vaults. Based on the data collected, essentially all lowpressure (

### <40psig) stations are located in vaults. Table 9-5 shows the number ofstations

**disagg~egated** by station type, inlet pressure range, and location in a vault versus above ground. 

### 9.3 CustomerMeters The total numberofcustomermeters 

### inthe U.S. gas industry, 56,132,300, and the number ofresidential customermeters, 51,524,600, were based on 

### Gas Facts, Table 8-1.2 The number ofresidential customer meters located indoors versus outdoors was estimated based on a regional breakdown oftotal customers presented in 

### Gas Facts. Based on data collected from 22 companies located in six regions withinthe United States, the majority ofcustomer meters inurban, northern areas ofthe country are located indoors, while the majority ofcustomer meters located in non-urban areas and in the southern areas are located outdoors. Onaverage, approximately 22% ofresidential 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 ofnegligible leakage from indoor meters is consistent with the findings from pressure regulating stations located in vaults.

8700 00 

### TABLE 9-5. SUMMARY OFACTIVITY FACTORS FOR DISTRIBUTIONMETERING/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-vaultcategories were combined for the lowpressure regulating station category.

### Therefore, the numberofoutdoor residential customer meters, 40,049,300, was 78% ofthe total population ofresidential customer meters. The confidence interval around the mean activity factor, 

### ± 10%, was estimated based on the variability in company supplied data. The precision ofthe total commercial/industrial customer meters is assumed to be 

### ± 5% ofthe estimated 4,608,000meters. 9.4 DistributionMainsand Services The total mileage·ofdistribution 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 purgedue to 

### pipeline abandonment, insta1Iation, and repair. The total miles ofmain pipeline in the gas industry, 836,700miles, was based on U.S. Department of Transportation (DOT) records." The total miles ofservice, 460,809miles, was reported by A.G.A.2 

### Therefore, the total combined main and service mileage in the United States is 1,297,569miles. The 90% confidence limitaround the activity factor is assumed to be 5%, or64,879miles.

89

### 10.0 RESULTS This section summarizes the majoractivity 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 orsite visit data. Most equipmentcounts taken at site visitsexcluded equipment that was not in use. The following tables (10-1 and 10-2) summarize the activity factor data. Previous sections ofthis report 

### explain the detailed basis for all ofthe activity factors. TABLE 10-1. WELL-DEFINED ACTIVITY FACTORS Segment Activity FactorName Number Total Industry 1992 Gross Gas Production (Tscf) 22.13 !992 Marketed Gas Production (Tscf) 18.71 Production No. ofGas Wells 276,000 No. ofOil Wells 602200 Processing No. ofGas Plants 726 Transmission and Storage Miles ofTrans Pipeline 284,500 No. ofStorage Facilities 475 No. ofStorage Wells 18,000 Distribution Miles ofService 460,800 Miles ofMains 836,760 Number ofResidential 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 ofGlycol Dehydrators 39,620 Production No. ofOil Wells Marketing Gas 209,000 No. ofGas Wells Requiring Unloading I !4,!00 Compressor Drivers 17,!00 recips Engine MMHp-hr 27,460 Offshore Platfonns I,I15 Dehydrators (% ofindustry dehydrators) 37,820 (95%) Separators 167,200 In-line Heaters 51,000 Total Production Vessels 256,000 Chemical Injection Pumps 16,970 Pressure ReliefValves 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 (% ofindustry dehydrators) 498 (1.3%) Acid Gas Recovery Units 371 Continued



### 91

### TABLE 10-2. (Continued) Segment Activity Factor Name Number Transmission and Compressor Drivers 7,715 recips Storage 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 (% ofindustry 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



### 9211.0 REFERENCES 1. Harrison, M.R., 

### H.J. Williamson, and L.M. Campbell. Methane Emissions from the Natural Gas Industry, Volume 3: General Methodology, 

### Final Report, GRJ-94/0257.20 and EPA-600/R-96-080c. Gas Research Institute and U.S. Envirorunentcl Protection Agency, June 1996. 2. American Gas Association. Gas Facts: 1993 Data, Arlington, VA, 1994. 3. Williamson, H.J., M.B. Hall, and M.R. Harrison. Methane Emissionsfrom the Natural Gas Industry, Volume 4: Statistical Methodology, Final Report, GRl-94/0257.21 and EPA-600/R-96-080d. Gas Research Institute and U.S. Environmental Protection Agency, June 1996. 4. 

### 5. 6. 

### 7. 8. 9. 10. II. 12. 13.
14. Cochran, William G. Sampling Techniques Third Edition, John Wiley & Sons, New York, 1977. U.S. Department ofEnergy/Energy Information Administration. Natural Gas Annual, 1992. DOE/EIA-0131 (89), Washington, DC, September 1993. GulfCoast Publishing Company, World Oil, Annual Forecast/Review, VoL 214, No. 2, February I993. Texas Railroad Commission Information Services Dept., Paula Middleton, Personal Contact with K. Hummel, Radian Corporation, June 1994. Alaska Oil & Gas Conservation Commission, Joan Miller, Phone Contact with Radian Corp., February 9, !995. Oil & Gas Journal. 1992 Worldwide Gas Processing Snrvey Database, !993. Texas Railroad Commission 1989, P-1, P-2 Tapes, Radian tiles. Dwight's Energy Data, Inc., Mike Morgan, 

### FAX contact with Radian Corp., December 29, !995. Offshore Data Services, David Sutherland, Phone Contact with Radian Corp., June 27, 1994. Minerals Management Service. User's Guide: MMS Outer Continental Shelf Activity Database (MOAD). OCS Study MMS 94-0018. U.S. Department of Interior, New Orleans, LA, June 1994. Hummel, 

### K.E., L.M. Campbell, and 

### M.R. Harrison. Methane Emissionsfrom the Natural Gas Industry, Volume 8: Equipment Leaks, Final Report, GRI94/0257.25 and EPA-600/R-96-080h. Gas Research Institute and U.S. Environmental Protection Agency, June 1996.
9315.
16.
17.

18. 19.

20.
21.

22. 23. 24. 25.

26. U.S. Department ofEnergy/Energy Information Agency. 

### Natura/ Gas Production Responses to a Changing Market Environment, 1978-1988. 

### Tab!e 6-3, Washington, D.C. Pipeline Systems Incorporated. 

### AnnualMethane Emission Estimate of the Natural Gas Systems in the UnitedStates, Phase 2. Prepared for Radian Corporation, September 1990. 

### Shires, T.M. and M.R. Harrison. Methane Emissionsfrom the Natural Gas Industry, Volume 7: Blow andPurge Activities, Final Report, GR!-94/0257.24 and EPA-600/R-96-080g. Gas Research Institute and U.S. Environmental Protection Agency, June 1996. Wright Killen & Co. 

### Natural Gas Dehydration: Status and Trends, Final Report, GRI-94/0099, Gas Research Institute, Chicago, IL, January 1994. Stapper, C.J. 

### Methane Emissionsfrom the Natural Gas Industry, Volume 11: Compressor Driver Exhaust, Final Report, GRI-94/0257.28 and EPA-600/R

### 96-0SOk. Gas Research Institute and U.S. Environmental Protection Agency, June 1996. 

### Tannehill, C.

### C. and C. Galvin. Business Characteristics of the Natural Gas Conditioning Industry, Topical Report, GRI-93/0342, prepared by Purvin & 

### Gertz, Inc, Gas Research Institute, May 1993. Dalrymple, D.A., F.D. Skinner, and N.P. Meserole. 

### Investigation ofU.S. 

### Natural Gas Reserve Demographics and Gas Treatment Processes, Topical Report, GRI-9l/00!9. Gas Research Institute, Chicago, IL, prepared by Radian Corporation, January 1991. Federal Energy Regulatory Commission (FERC) Form No. 2: Annual Report ofMajor Natural Gas Companies, 1992 database. American Gas Association. 

### Natural Gas Industry Environmental Organization Structure Survey, Arlington, VA, 1990. American Gas Association. 

### Gas Facts: 1991 Data, Arlington, VA, 1992. Shires, T.M. and M.R. Harrison. 

### Methane Emissionsfrom the Natural Gas Industry, Volume 12: Pneumatic Devices, Final Report, GRI-94/0257.29 and EPA-600/R-96-0801. Gas Research Institute and U.S. Environmental Protection Agency, June 1996. Biederman, N. GR! TRANSDAT Database: Compressor Module. (prepared for Gas Research Institute) npb Associates with Tom Joyce and Associates, Chicago, IL, August 1991.

9427. 28. 29. Texas Mid-Continent Oil and Gas Association (TMOGA) and Gas Processors Association (GPA), Dehydrator Survey Database, 1991. U.S. Department ofTransportation, Research and Special Programs Administration, 1991. Southern Cross Corporation. "Comments on Docket PS-123 Notice I, Leakage Surveys," 

### 49 CFR Part 192, Department ofTransportation, Research and Special Programs Administration, Materials Transportation Bureau, Office of Pipeline Safety Regulations, December 19, 1991.



### 95

### APPENDIX A Site Data

A-1TABLE 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•• •Region Key: GC = GulfCoast; CP = Central Plains; PM = Pacific Mountain; AGL = Atlantic & Great Lakes. **This does not include ail sites visited·by Star or otherfugitive 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 SurveyType 

### 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 EquipmentCounts: Gas Wells 2 0 80 0 0 12 Oil Wells 3 150 0 40 22 0

+Oil wells that marketgas 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 3ph Flash 1 +Dehywitb VentControl 0 +Dehy w/Kimray

**~mps** 1 +Dehy w/Stripping Gas 0 Miles ofGatbering Pipeline - - - - - FugitiveComponentcount y N N N N N Vented (Site Blow & Purge Data) y y y y y y Notes: 1) SurveyType V = Site Visit (Radian); P = PhoneSurvey: S = Sue Visit (Star). l) * = Gas lift
**cumpre~~urs** not included. J) Y =Yes. N =No;·-·"' No Data. 4) Region Key: GC = GulfCoast PM = Pacific Mountain. Total Equiprnenll OfT short! Totals TotalWells &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 GasWells (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 0.03TABU: A-3. PRODUCTION SITES (GulfCoast

**On~hore** nata)
Region GC GC GC GC GC GC GC GC GC TotalGC Site 7 8 

# • 10 11 12 13 14 15 Equip./ Equip./ Company 5 

# • 7 8 9 10 11 11 11 9Sites Equip./ Equip./ Mkt. Gas Prod. Gas SurveyType 

### v v v v v v v v v 7 Companies Total TotalWells GasWells (1/MMcfd) (1/Mi\fcfd) Gas Marketed(MMcfd) 23.1 25_5 124 54 28 250 1.9 7 130 643.4 0.54 GasProduced (MMcfd) 23.1 2S.S 124 

# " 28 250 1.9 7 130 643.4 0.54 EquipmentCounts: GasWells 13 80 18 130 26 300 0 10 31 608 Oil Wells 50 0 

# ' ' 0 300 

# "' 127 0 638 +Oil wells !hatmarketgas so 0 3 3 0 300 155 68 0 579 Separators 

# " 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 Chernlnj 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 +Dehywith Yen!Control 4 0 2 0 0 0 0 0 0 6 +Dehyw/Kimray Fumps 7 I I 6 26 2 0 0 26 69 +Dehy w/Srripping Gas 0 0 0 0 0 0 0 0 0 0 Miles ofGatheringPipeline
- 46.3 26.4 40 8 - - - FugitiveComponentcount y y y y y y
- - - **\',~nted** {Site Blow& PurgeData) y y y y y y y y y Notes: I) SurveyType V = Sire Visit (Radian): P = Phone Survey;S =SiteVisit {S!ar). 2) • = Gas lift compressorsnot included. 3) Y =Yes, N =No; •.· = NoDara. 4) Region Key: GC = GulfCoast.-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 SurveyType 

### 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 EquipmentCounts: Gas Wells 138 321 !000 400 I 100 Oil Wells 55 II 0 0 0 0 +Oil wells that marketgas 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 Chemlnj Pumps 28 273 0 13 0 0 Compressors• 31 50 64 Dehydrators 16 220 0 400 0 25

+Dehywith 3 phFlash 0 0 0 0 +Deitywith Vent Control 0 0 0 . +Dchy w/Kimray Pumps 16 220 0 25 +Dehyw/Stripping Gas 0 0 0 MilesofGathering Pipeline 5.2 

### . 600 . - . Fugitive Componentcount y y y . - . Vented (Site Blow& Purge Data) y y y
Notes: I) SurveyType V =Site Visit {Radian); P = Phone Survey; S =Site Visit {Star). 2) * = Gas lift compressorsnot included
. .l) Y '" Yes. N = N(>; -.- =No 0dta. 4) Region Key: CP = Central Plams. CP TotalCP 

# " 18 7 Sites Equip.! p 7 Companies Total TotalWells 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 GasWells (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 SuneyType 

### v v v v v v 

### v Gas Markered (MMcfd) 4 104 0.138 0.03 O.o2 0.035 0.8 GasProduced(MMcfd) 4 307 0.138 0.03 0.02 0.035 0.8 EquipmentCounts: Gas Wells S3 0 0 0 0 0 0 Oil Wells 0 913 18 8 10 

# " 20 +Oil wells that marketgas 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 Chernfnj Pumps 36 0 0 0 0 0 0

**Comprcsso~•** 17 IY 0 0 0 0 I Dehydrators 5 0 0 0 0 0 I +Dehywith 3ph Flash 0 0 0 +Dehy with VentControl 0 .o 0 +Dehyw/Kimr.ty Pumps 5 0 I +DehywfStrippingGas 0 0 0 Miles ofGathering Pipeline . 

### . . . . . FugitiveComponentcount y y N N N N N Vented (Site Blow & Purge Data) y y N N N N N Notes: I) SurveyType V =Site Visit (Radian); P =Phone Survey; S = SiteVisit (Star). 2) • = Gas lift compressors not included. Jl Y = Yes. N =No:·.· =No Data. 4) Region Key- PM = Pacific Mountam. PM PM TotalPM 30 31 21 21 9 Sftf:! Eqllip.l 

### v v 4 CompanJes Total TotalWells 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 GasWells (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.Q2TABLE 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 

# " 39 40 Company 

# " 24 25 26 27 28 29 30 JO SurveyType 

### v p p 

### v p p F 

### s s Gas Marketed (MMcfd) 24 6 15 17 12 16 81 O_!g O.IS GasThroughput(MMcfd) 24 6 IS 17 12 20 81 0.19 0.19 OilThroughput(1000 BID) 0 EquipmentCounts: GasWel!s 800 250 1000 520 450 1582 4034 II II Oi! Wells 0 0 0 0 163 418 0 0 0 +Oil wells that marketgas 0 0 0 163 418 0 0 0 Sepanttors lSI 250 500 520 4SO 1582 3227

- 2 In-line Heaters 0 0 PneumaticDevices 76 0 10 520 450 1582 1294 - Chernlnj Pumps 0 0 0 12 0 8 

# " y 0 Compressors• I - - - - - - 0 Dehydnttors 0 2 I 30 0 0 

# ., +Dehy with 3 ph Flash 0 0 0 0 

# ' +Dehywith VentC:mtrol 0 

# ' 2 .+Dehyw/K.imnty Pumps 0 2 I 30 8 0 +Dehyw/Stripping Gas 0 21 0 MilesofGathering Pipeline
- 

## - - - - - Fugitive Componenlcount y N N N y y y y Vented (Site Blow& Purge Data)
- - y y
-

-- Notes: I) SurveyTypeV = Site Visit {Radian); P = Phone Survey; S = Site Visit (Star). (Continued) 2) • = Gaslift 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 41 42 43 44 45 46 47 

# .. .. 51) Company 30 31 31 31 31 31 31 31 

# " 31 19Sites SuneyType 

### s s s s s 

### s s 

### s s s 10Companies GasMarketed(MMcfd) 0.17 0.39 0.37 0.37 0.18 0.23 0.30 0.35 0.13 0.35 GasThroughput(MMcfd) 0.17 0.39 0.37 0.37 0.19 0.23 0.30 0.35 0.13 0.35 OilThroughput(IOOOBID) EquipmentCounts: GasWells 10 23 22 22 II 14 18 21 8 21 Oil Wells 

# • 

# • • • • 0 0 0 0 0 +Oil wellsthat market gas 0 0 0 0 0 0 0 0 0 0 Sepanuors 0 10 7 8 5 3 

# " 17 

# ' 7 In-line Heaters 0 0 0 0 I I 0 0 0 0 Pneumatic Devices
- - - - - - - - - - ChernInj Pumps 0 0 0 0 0 0 0 0 0 0 Compressor:s* 0 0 0 0 0 

# • 0 0 0 0 Dehydrators

- - - 

### - - - - - - - +Dehywith 3 ph Aash +Dehywith VentControl +Debyw/JGmrny Pumps +Dehyw/StrippingGas Miles ofGatheringPipeline - - - - - 

### - - - - - FuririveComponentcount y y y y y y y y y y

**v~nted** (Site Blow& Purge Data) - - - - - - Notes: 1J SurveyType V "' Site Visit (Radian); P == Phone Survey;S "' Site Visit (Star). 2) • == Gas lift compressorsnot included. 3) Y == Yes, N =No;"-" = No Data. 4) Region Key: AGL "' Atlanlic & Great Lakes. Equip.( Equip.! Total Equip./ Equip.! Mkt. Gas Prod.Gas (SiiM32-50) TotalWells GasWdls (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 21TABLE 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 ofcompressor blowdown open-ended lines, site open-ended lines, and compressor pressure reliefvalves. 

### ** 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 -VaporRecovery 0 0

- Vent Flash Gas Pneum 48 12 

## - 8
- 20 75 40 68 83 50 Wells 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

- StrippingGas 0

- Vapor Recovery 0

- Vent Flash Gas Pneum 3 Wells FugitiveCC 1123 134 Site R.D Practices y

- Co. BID

**No'~** - - Fugcc does not includeconnectionsor tubing 

### * "" Elec driven compressors 

### ** = Not includinghydraulic valves 

### *** = Recip Enginew/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 ortubing • = 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 

### Methodsfor Estimating Activity Factors

B-1

### B.l RATIO (SITE-WEIGHTED) METHOD FOR ESTIMATION OFAN ACTIVITY FACTOR As discussed in Section 3.4, the ratio (site-weighted) method has been used to estimate activity factors on the basis ofwell counts orproduction. In that section, a 

### nwnerica1 example is given which illustrates the ratio method for that purpose. This appendix provides a further description ofthe ratio method, including calculation ofa 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 theRatio Method 

### Suppose y, 

### x,

n X N 

### device count (e.g., number ofseparators) at the ith 

### sampled site; value ofthe extrapolation parameter (number

## otwells or gas production) at the ith 

### site; number ofsites sampled; the regional value ofthe extrapolation parameter, e.g. the total number ofwells in the region; and the total number ofsites in the region. For the purposes ofillustration, this section discusses the estimation ofthe regional number ofseparators by using the well method. Then, by the ratio method, the following is the estimate ofthe number ofseparators per well:



### or B-2

### "

This estimated number ofseparators per well and the regional number ofwells is then used to estimate the number ofseparators in the region:

B.1.2 Confidence Interval for Estimate Produced by the Ratio Method 

### Cochran presents two approaches for estimation on the basis ofconfidence intervals. One method for calculation ofthe 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 

### ifthe sample size (the number ofsites visited in our application) is sufficiently large (at least 30) and the relative uncertainties (coefficients of variation) in both the average number ofseparators per site and the average number of wells per site are both sufficiently small (less than 10%). The suggested rules ofthumb are given by Codrran. Ifthe ratio is normally distributed, its confidence interval will be symmetric. Ifthe ratio itselfis 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 ofthe asymmetry in some applications is a fundamental consideration in the selection ofa method.

8-3

### 90% Confidence 

### Limit 

### mean X



### Emissions 

### 90% Confidence 

### Limit



## a) NormalDistribution 90% Confidence 

### Limit 

### mean 

# x

Emissions 

### 90% Confidence 

### Limit



## b) Lognormal Distribution

FigureB-1. 

### Conceptual Comparison ofNormal and Lognormal Distributions B-4For 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 ofzero. The possibility ofvalues extremely larger than the true value, without a corresponding possibility ofvalues 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 ofproducing an upper confidence level that is Wlreasonably large from an engineering point ofview (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 engineeringjudgement 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 ourapplication, the denominator is the sum ofthe production levels or ofthe numbers ofwells for the sites visited in a region. From a practical perspective, it is not reasonable to expect that either ofthese sums can become arbitrarily close to zero, causing an extremely large ratio ofseparators per well or separators per unit ofproduction. The number ofwells at a site ofinterest must be at least one. Thus, the sum ofthe numbers ofwells has a lower bound equal to the number ofsites visited. The production does not have a definable lower bound ofthis 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 ofdevices per unit ofproduction on the basis ofthe data for all sites. 

### B-5The argwnent above pertains to the possibility ofan arbitrarily small denominator, which could cause extreme skevmess; Figure B-2 depicts ahypothetical distribution that is skewed, or asymmetric. The relationship between the number ofdevices and the number ofwells or amount ofproduction is also relevant. Theoretically, positive skewness in a ratio could result from positive skewness in the numerator; this would be a special concern ifthe numerator could increase without bound, independently ofthe value ofthe denominator. In this applica:ion, however, 

### itis not reasonable to expect that the number ofseparators attached to a given well is unbounded; similar comments apply for other device types. Further, it is not reasonable to expect that the number ofseparators at the visited sites in a region is independent ofthe total production at those sites and can become arbitrarily large, independently ofthe 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. Foreach ofa selected set ofregions and device types, the number ofdevices was divided by the extrapolation parameter (wells orproduction) for each site. This produced a ratio for each site visited for a given region and device type. In most cases, the number ofsites is too small to allow a detailed characterization ofthe distribution. For separators for the Atlantic/Great Lakes region, however, there were 19 sites. The distribution ofseparators per well is displayed for this case in Figure B-3. The histogram is somewhat ragged, because ofthe sample size; even 19 is a small sample size to characterize a distribution. Nevertheless, there is no evidence ofpositive skewness. Despite the raggedness ofthis 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 ofseparators per unit of production for the same region. In this case, there is evidence ofasymmetry ·in the distribution ofthe 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

### (%, NormalAssumption)
--A--- 

### Emissions ......,.._.. Normal Limit 

### --e-- Lognormal Limit Figure B-2. Comparison of90% 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 ofSeparators perWell

B-8

# I01 

# 91 R

### j
u 

# 1 7j

**~** 

# 6

**~** z

**~**

**~** 0

**~**

**~**

**~**

10 30 50 70 90 Separators/Production (MMcfd) 

### Figure B-4. Ratio ofSeparators perUnit ofProduction

B-9impression ofasymmetry is a single site with a ratio of99.9 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 ofproduction. 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 ofproduction, so the ratio is 39.8 separators per MMcfd. The site values ofthe number ofseparators per we!l for the Central Plains region revealed evidence ofnegative skewness. That is, instead ofa long tail to the right, as in Figure B-4, there was some evidence ofa long tail to the left. Since there were only seven sites, a histogram ofthis data set would not be meaningful and is not shown. In this case, method 2 produced an upper confidence limit for the ratio ofseparators per well that was unreasonably large in this case from an engineering or a statistical point ofview. This upper limit was several times the largest site ratio ofseparators 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 ofwhy method I was preferred over method 2. Moreover, asymmetric uncertainties ofindividual parameters exist for other reasons. Confidence intervals with greater than IOO% uncertainty exist for activity factors, emission factors, oremission rates for some source categories. One possible explanation is that the error in the estimated parameter is not nonnally distributed. The ultimate objective ofthe study, however, is to estimate the national emission rate. The sum ofthe emission rates for 82 source categories will tend toward nonnality, even ifsome ofthe individual values summed are nonnonnal. Thus, even ifsome category parameters were not nonnal, this would not necessarily invalidate the confidence interval for the national emission rate. Moreover, an assessment has been made ofthe effect ofa 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 ofeither·assumption (Section A.4). B-10

### Based on a finite sample ofsize n (i.e., n sampled sites), the following is an approximation ofthe variance ofthe error in Y R:



### The quantity N, the total nunber ofsites in the region, is not known and, therefore, must be estimated. The total number ofseparators, X, in the region is known. The quantity X divided by the average number ofseparators per site is an approximation of the number ofsites in the region. This me:hod ofestimating N was suggested to Radian by Jonathan Cohen ofICF 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 ofYR• 

### 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 YR 

### is as follows:

where z is a tabulated value ofthe 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 ofthe variance [v(YJJ can be neglected. The use ofthe z statistic is generally accepted ifthe sample size is greater than 30. According to Cochran's rules ofthumb, 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 ifthe 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 ofconservatism; 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 OFAN ACTIVITY FACTOR The methods discussed in the preceding section were used to est.irnate"the activity factor and its uncertainty on the basis ofboth well counts and production. The arithmetic average ofthe two estimates 

### was computed to obtain the final estimate. The two estimates are based ondifferent extrapolation factors (values ofthe 

### x) but common device counts (values ofthe y;). The device counts vary by site and are subject to sampling error. Thus, this source ofsampling error was conunon to the two estimates ofthe activity factor. It has been discussed elsewhere that separate measured quantities (e.g., emission rates from different types ofdevices) 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 ofwells at the ilh 

### site; production at this site;

= 

### estimate ofR on the basis ofwells;

B-12

## R, estimate ofRon the basis ofproduction; 

### YR.w

**~** 

### estimate of

### Y R

### on the basis ofwells; and

**Y~, ~**  estimate of

### Y R on the basis ofproduction. By substituting xw., for X; in the appropriate equations in the preceding section, for example, one obtains the estimate ofthe number ofdevices in the region on the basis ofwells and the confidence interval for this estimate. The following is a sample estimate ofthe covariance between the errors in the two estimates:

This expression satisfies important required properties ofthe covariance, such as the symmetry property:

Additionally, the covariance between a quantity and itselfequals the variance ofthat 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 ofthe 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 ofthe two estimates ofN was used, both where N appears explicitly in the covariance equation and in calculating f. Now, the expression for the final estimate ofthe activity based on the arithmetic average approach is as follows: The variance ofthis 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 ofsites for which data existed for both wells and production did not coincide exactly. Inthese cases, the covariance was computed on 

### the basis ofthe sites for which com . 'non data did

### exist. This provided a somewhat conservative (large) estimate ofthe covariance. This calculation ofthe 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 ofindependence between the estimates ofYR based on the two extrapolation parameters. The somewhat conservative covariance estimate produces a somewhat conservative confidence interval for the final estimate ofYR· 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 ofthe 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-14shown 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 ofan 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 ofthe confidence interval given earlier ifthe sites for which data exist for wells and production are the same.

B-15

### APPENDIX C Conversion Table

C-1Unit Conversion Table English to Metric Conversions 

### 1scfmethane

**~** 

### 19.23 g methane 1Bscfmethane

**~** 

### 0.01923 Tg methane I Bscfmethane

**~** 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

**~** 

### 1055joules 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 ofany gas:



### MMTCE = (MMT 

### ofgas) 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) ofC02

**~** 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 (109 

### set). MMscf

**~** 

### Million standard cubic feet. Mscf

**~** 

### Thousand standard cubic feet. Tg

**~** 

### Teragram (1012 

### g). Giga (G)

**~** 

### Same as billion (109). 

### Metric tonnes

**~** 1000 

### kg. pstg

**~** 

### Gauge pressure. psra

**~** 

### Absolute pressure (note psia = 

### psig 

### + atmospheric pressure). GWP

**~** 

### Global Warming Potential ofa particular greenhouse gas for a given time period. MMT

**~** 

### Million metric tonnes ofa gas. MMTCE

**~** 

### Million metric tonnes, carbon equivalent. MMT ofC02

### eq.

**~** 

### Million metric tonnes, carbon dioxide equivalent.

C-3TECHNtCAL REPORT DATA (Pitmt!rudllll:tructiOnsontht:TnUrt!M{Ol't!complt:tiltg} 1.REPORTNO. 

# I' 3.RECIPIENT'SACCESSIONNO. 

### EPA-600/R-96-080e 4 TITLE ANOSVBTITLE 5.REPORTDATE Methane Emissions from the Natural Gas Industry, 

### June 1996

Volumes 1-15 (Volume 5: Activity Factors} 6.PERFORMINGORGANIZATIONCODE 7.AuTHORCSl L.
**Cam~bell.** M.Cam/bell, M.

**Cow~ll,**D.Ep- B.PERFORMINGORGANIZATIONREPORTNO.I

**~erson,** M.HalL •Harrison, •Hummel.D. yers. 

### DCN 96-263-081-17 

# I

•Shires,B.Stapper, C.Stapper.J.Wessels. and 

## * "·PERFORMINGORGANIZATIONNAMEANDADDRESS 10.PROGRAMELEMENTNO. RadianInter:c:ational LLC 

### P.0. 

### Box 201088 11.CONTRAC /GRANTNO. Austin. Texas 

### 78720-1088 5091-251-2171 (GRI) 68-Dl-0031 (EPA) 12,SPONSORINGAGENCYNAMEANDAOOFIESS 13.TYPEOFR'POATiNCPERIODCOVERED 

### EPA, Office of Research andDevelopment Final; 3 91-4 96 14.SPONSORINGAGENCYCODE Air Pollution Prevention and Control Division 

### Research Triangle Park, NC 

### 277!1 EPA/600/13 ts.suPPLEMENTAAYNOTes EPA project officer is D.A.Kirchgessner.MD-63.919/541-4021. Cosponsor GRI project officer is R.A.Lott, Gas ResearchInstitute, 8600 West Bryn 

### Mawr Ave., Chicago, 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 lvear. The objective was to determine CH4 emissions from the wellhead and ending


### ~ownstream at the customer's meter. The accuracygoal was to determine these temissions within

### +/-0.5% ofnatural gas production for a 90% confidence interval. F01



### ~e 1992 base year, total CH4 emissions for the U.S. natural gas industrywas 314 

### +I- 105 Bscf 

### (6.04 

### +I- 2.Ol Tg). This is equivalent to l.

### 4 

### +/- 0.5% ofgross natural gas production. and reflects neither emissions reductions (per the voluntary AmeriGas Association/EPA Star Program) nor incremental increases (due to increased gas usage) since 1992. Results from this program were used to compare greenhouse bas emissions from the fuel cyclefor 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 theIPCC and others. In addition. study results are being used by the natural gas industry to reduce operating costs while reducing emissions. 

# "· KEYWORDSANDDOCUMENTANAl..YSIS 

## •• DESCRIPTORS b.IDENTIFIERS/OPENENDEDTERMS 

## '· COSATl Field/Group Pollution jPollution Prevention 

### 13B Emission Stationary Sources 

### 14G Greenhouse 

### Effect Global Warming 

### 04A Natural Gas 

### 2lD Gas Pipelines 

### 15E Methane



### ~7C 18.OISTRJSUTJONSTATEMENT 19.SECURITYCLASS (ThURt:pDn} 21.NO.OFPAGES 

### Unclassified 137 Release to Public 20.SECURITYCLASS {ThUpt>gt:) 22.PRICE 

## I Unclassified ' EPAForm 2220·1 (9·73) 

### C-4U.S. ENVIRONMENTAL PROTECTIONAGENCY Office ofResearchand Development NationalRiskManagementResearchLaboratory TechnologyTransferand SupportDivision Cincinnati, Ohio45268 OFFICIAL BUSINESS PENALTY FOR PRIVATE USE. :5300 AN EQUAL OPPORTUNITY EMPLOYER

Ifyour 6ddrus is incorrect.**pi~II!St** changeon the above label tearoff; andteturn to the above 11ddress. Ifyou donot desire to continue receiving these teclfnical reports. CHECKHER£0; tear offlabel, andreturn it to the above address. 

## Publication No. EPA-600/R-96-0BOe