reconciling top-down and bottom-up methane emission estimates from onshore oil and gas ... ·...

Final Report - Contract Number: 12122-95

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Final Report 2

Reconciling Top-down and Bottom-up 3

Methane Emission Estimates from Onshore Oil 4

and Gas Development in Multiple Basins: 5

Report on Fayetteville Shale Study 6

RPSEA Contract Number: 12122-95 7

September 30, 2016 8

Authors: 9

Daniel Zimmerle1, Gabrielle Pétron2, Cody Pickering1, Tim Vaughn1, Clay Bell1, Stefan 10 Schwietzke2, Ingrid Mielke-Maday2, Garvin Heath3, Dag Nummedal4, Cynthia Howell4, 11

Prime Contractor: 12

Dag Nummedal, PI, Colorado School of Mines, 1100 Illinois St., Golden Co. 80401 13

1 Colorado State University 2 National Oceanic and Atmospheric Administration & University of Colorado Boulder 3 National Renewable Energy Laboratory 4 Colorado School of Mines


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LEGAL NOTICE 14

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This report was prepared by Colorado School of Mines as an account of work 16 sponsored by the Research Partnership to Secure Energy for America, RPSEA. 17 Neither RPSEA, members of RPSEA, the National Energy Technology Laboratory, 18 the U.S. Department of Energy, nor any person acting on behalf of any of the entities: 19

a. MAKES ANY WARRANTY OR REPRESENTATION, EXPRESS OR 20 IMPLIED WITH RESPECT TO ACCURACY, COMPLETENESS, OR 21 USEFULNESS OF THE INFORMATION CONTAINED IN THIS 22 DOCUMENT, OR THAT THE USE OF ANY INFORMATION, 23 APPARATUS, METHOD, OR PROCESS DISCLOSED IN THIS 24 DOCUMENT MAY NOT INFRINGE PRIVATELY OWNED RIGHTS, 25 OR 26

b. ASSUMES ANY LIABILITY WITH RESPECT TO THE USE OF, OR 27 FOR ANY AND ALL DAMAGES RESULTING FROM THE USE OF, 28 ANY INFORMATION, APPARATUS, METHOD, OR PROCESS 29 DISCLOSED IN THIS DOCUMENT. 30

THIS IS AN INTERIM REPORT. THEREFORE, ANY DATA, CALCULATIONS, OR 31 CONCLUSIONS REPORTED HEREIN SHOULD BE TREATED AS PRELIMINARY. 32

REFERENCE TO TRADE NAMES OR SPECIFIC COMMERCIAL PRODUCTS, 33 COMMODITIES, OR SERVICES IN THIS REPORT DOES NOT REPRESENT OR 34 CONSTIITUTE AND ENDORSEMENT, RECOMMENDATION, OR FAVORING 35 BY RPSEA OR ITS CONTRACTORS OF THE SPECIFIC COMMERCIAL 36 PRODUCT, COMMODITY, OR SERVICE. 37

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Abstract/Executive Summary 40

Prior methane measurement studies or analyses have indicated that top-down methods have 41 consistently reported higher total emissions or emissions intensity rates than bottom-up or 42 inventory-based method. A four-week intensive field campaign was conducted in the eastern 43 Fayetteville Shale region of the eastern Anadarko Basin in Arkansas. The study design 44 developed independent estimates of methane emissions from onshore natural gas systems 45 using contemporaneous, multi-scale, measurement approaches. These estimates were then 46 compared for the purpose of reconciling the estimates derived from various methodologies at 47 both the study-region and facility scale. The study was designed to understand, and potentially 48 reconcile, the persistent gap between top-down and bottom-up methane emissions estimates 49 for production regions. 50

To minimize the potential shortcomings of the prior studies, the current study was designed as 51 a first-of-its kind to conduct contemporaneous measurements at the device-, facility-, and 52 regional-scale with site-access and activity and emission data input from local natural gas 53 operators. Onsite device measurements utilized traditional detection and quantification 54 methods. Three methods were utilized for facility-scale measurements: EPA Other Test 55 Method 33a (OTM33a), dual tracer flux method, and aircraft spiral-flight mass-balance method. 56 Regional-scale measurements utilized the aircraft mass-balance method. Contemporaneous 57 measurements at several scales supported a robust comparison between the different 58 estimation methods. Measurements were conducted for most of the supply chain of the 59 natural gas industry in the basin: production (wells), gathering pipelines and compressor 60 stations, transmission compressor stations, transmission-distribution transfer facilities, and 61 distribution. The study area did not include natural gas storage facilities or processing plants, 62 and no direct measurements were made of transmission pipelines due to regulation and 63 maintenance that ensures minimal fugitive emissions from transmission pipelines. This project 64 provides a unique dataset on facility-level emissions detection and measurements with on-site 65 and downwind methods. Detailed comparisons are done for production sites and gathering 66 stations. Direct comparisons were made at the facility level between total emissions estimated 67 from contemporaneous downwind ground or airborne measurements and the sum of detailed 68 on-site emission measurements and simulated emissions for a few source categories not 69 measured. The study also includes the only recent measurements made on gathering pipeline 70 networks. 71

The study also developed a temporally- and spatially-differentiated, bottom-up model of the 72 study area CH4 emissions. This model included non-oil & gas CH4 emission sources, albeit at 73 less resolution. Using extensive activity data provided by study partners, substantial hourly, 74 diurnal and weekly variations in emission rates were observed that have not been captured in 75 prior bottom-up modeling efforts. The assessment of these variations provided key insights 76 and conclusions related to some of the discrepancies between past top-down and bottom-up 77 emission comparison efforts. Steady horizontal wind speed and direction over the study area 78 during two aircraft mass balance flights, coupled with detailed spatial modeling support a sub-79


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area comparison between bottom-up and top-down emission estimates on two successive 80 days. We compared the aircraft mass balance estimate with an hourly bottom-up emission rate 81 estimate for these two days, and do not extrapolate the aircraft mass balance or the bottom-up 82 model to an annual emission estimate. This is the first time this type of spatial and day-to-day 83 estimates comparison can be done for a shale gas producing basin. Flask and in-situ CH4 and 84 C2H6 measurements from the aircraft and ground vehicles were made to assist attribution of 85 study area emissions to anthropogenic and biogenic CH4 sources. 86

Measurements from the field campaign exhibit skewed (‘long tail’) emissions distributions in 87 most device and facility categories, in alignment with data reported from most previous 88 studies. More complete activity data provided through the participation of study partners 89 allowed a more accurate attribution of emission sources, including large emitters, to abnormal 90 process conditions, episodic releases, and other operationally-specific conditions. Further, 91 these activity data also provided a more complete and accurate representation of the diurnal 92 and weekly variations in episodic emissions. 93

Several sectors made minor contributions to total study area emission estimates, including 94 distribution and gathering pipelines. As with other producing basin-level studies, production 95 and gathering constituted the majority of natural gas sector emissions. 96

Overall, the study was able to close in on some of the differences between top-down and 97 bottom-up regional scale estimation methods and provide factual evidence for important 98 requirements to reconcile estimates in the study area. The study advanced both facility and 99 basin-level field research in several key areas. Comparison of contemporaneous measurements 100 of facility emissions from multiple methods provides insight into the performance of the 101 measurement methods and the degree of agreement between top-down atmospheric 102 transport methods and traditional bottom-up measurement and modeling methodologies. 103 Annexes to the report provide detailed information on study protocols for both measurement 104 and modeling. 105

One of the critical factors that made this project unique was the unprecedented data sharing 106 and site access to the various segments of the natural gas value chain in the study area. Study 107 teams had site access to 100% of the distribution system, 82% of production wells, 74% of 108 gathering stations, and 73% of transmission stations. Operational data was provided for 100% 109 of the distribution system, 99% of production wells, 100% of gathering stations and 73% of 110 transmission stations. As a result, the project was able to incorporate better data quality and 111 prevailing operator practices into its analysis, which was critical in closing the gap between top-112 down and bottom up methane emission estimates. This collaboration between government, 113 academia and industry may be a valuable model for future collaboration in solving important 114 technical problems of national and regional interests. 115

Finally, in addition to results reported herein, nine peer-reviewed papers are in progress and 116 are described in the final section of the report. 117


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Acknowledgements 118

Funding for this work was provided by RPSEA/NETL contract no 12122-95/DE-AC26-07NT42677 119 to the Colorado School of Mines. This study was also supported by financial and in-kind 120 contributions by Southwestern Energy, XTO Energy, a subsidiary of ExxonMobil, Chevron, 121 Statoil, the American Gas Association and state cost-share through the Colorado Energy 122 Research Collaboratory (a consortium of the Colorado School of Mines, Colorado State 123 University, the University of Colorado Boulder and the National Renewable Energy Laboratory). 124 The National Oceanic and Atmospheric Administration provided significant in-kind support and 125 funding from the NOAA Climate Program Office grant NA14OAR4310142. In kind support was 126 received through the National Science Foundation AirWaterGas Sustainability Research Project 127 (Cooperative Agreement No. CBET-1240584). 128

In addition, XTO Energy, Southwestern Energy, CenterPoint, Enable Midstream Partners, Kinder 129 Morgan, and BHP Billiton provided site access and/or confidential data to support the study. 130


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Acronyms and Standard Terminology 131

Acronyms 132

Standard Terminology 133

Geographic & location terms: 134

Study Area – geographic area which was studied. The study area is a sub-set of the 135 Fayetteville Shale Play in the eastern part of the Arkoma Basin. 136

Facility – a geographically compact set of equipment that can be reasonably isolated 137 from other facilities for measurement purposes. Examples: a compressor station, well 138 pad, chicken farm, pig launcher, etc. 139

Unit – a major component of a facility, typically with its own identifier. For example, a 140 compressor skid, consisting of an engine driver and the compressor itself, is a unit in a 141 compressor station facility. 142

Fence line – a well-defined border surrounding a facility, typically the property line of 143 the facility, whether there is an actual fence there or not. 144

Pipeline – above- and below-ground infrastructure for moving natural gas, oil, water or 145 other fluids between facilities. 146

Right of Way (ROW) – land on which a pipeline is located, and over which the pipeline 147 operator has some amount of control. Multiple pipelines can be in a ROW, but all ROWs 148 measured in this study had only one pipeline in them. 149

Well Pad – a piece of land, sometimes graveled, on which one finds one or more wells 150 and associated gas and liquid handling equipment. A well pad is a facility. A well is not a 151 facility. A well pad has a fence line. A well does not. 152

Qualitative and quantitative measurement and estimates of emissions: 153

Unless stated otherwise, emission in this report refers to methane emissions, and does 154 not include other gas species. 155

Emission rate – mass flow of gas (CH4) per unit time, in mass rate units such as kg/h or 156 scfm (standard cubic feet per minute). 157

Emission flux – mass flow of gas (CH4) across a unit area per unit time, in mass flux units 158 such as kg/m2/h. 159

Onsite measurements – quantitative per-device measurements made by ground teams 160 moving through the site with instruments that make direct measurements. Quantifies 161 an emission rate on per-device basis. 162

o ODM – onsite device measurement. 163


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Downwind measurements – quantitative ground-level measurements using tracer or 164 OTM methods within approximately 2 kilometers or ~ 1.2 miles of an emission point. 165 The method consists of data collected in the field and processed with a defined protocol 166 to estimate an emission rate. 167

o DFE – downwind facility estimate, using either method. 168

o TFE – tracer facility estimate 169

o OFE – OTM33A facility estimate 170

Mobile measurements– Ground-level concentration measurements mixing ratio 171 measurements using instruments downwind of an emission point. Does not quantify an 172 emissions rate. 173

(Aircraft) Spiral or Spiral Flight – an airborne measurement method which estimates the 174 emission rate from a facility using mixing ratio and wind measurements made during a 175 spiral pattern aircraft flight around a facility. 176

o AFE – aircraft facility estimate for a facility (or if the flight path is too large – a 177 group of facilities) in the footprint of the aircraft spiral flight 178

Aircraft Area Estimate (AAE) or Aircraft Mass Balance (AMB) – quantitative estimate of 179 emission flux and emission rate, from the study area using a mass balance box model. 180

Engineering Estimate – an estimate of an emission rate/flux that was not measured in 181 the field campaign. Uses engineering methods, possibly with data from other published 182 sources (e.g. an emission factor). 183

GHGRP or GHGI Estimate – an estimate of emissions using the methods required by the 184 EPA Subpart W GHG Reporting (GHGRP) or EPA’s GHG Inventory (GHGI) for the facility 185 or area being estimated. These estimates utilizing the method defined in the EPA 186 program, but typically utilize activity data collected and defined in the project. 187 Estimates are not equivalent to reported data for a company, basin or region, due to 188 this difference in activity drivers. 189

Composite estimates of emissions rate: 190

Study Onsite Estimate (SOE) – the most “complete as possible” estimate of a facility’s 191 emission rate utilizing available onsite device measurements (ODMs) and engineering 192 estimates for any unmeasured sources. 193

Ground-Level Area Estimate (GLAE) or Study Area Estimate (SAE) – a temporally-specific 194 estimate of an area’s emission rate using any complete combination of downwind, on-195 site and engineering rate estimates. 196


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Comparisons: 197

Facility Emissions Comparison (no abbreviation) – an apples to apples comparison 198 between any available combination of SOE/AFE/TFE/OFE. Note, the comparison must 199 be comparing complete estimates of the facility’s total emissions. 200

Study Area Emissions Comparison (no abbreviation) – temporally-specific comparison 201 between a ground-level and aircraft mass balance estimates. 202

Classes of emissions: 203

Episodic Emission or Episodic Emission Source (no abbreviation) – an emission which 204 occurs for a defined period of time (i.e. has a defined beginning and end). 205

Steady-State Emissions (no abbreviation) – emissions which are estimated to occur at a 206 more-or-less continuous level for an extended period; also, equivalent to all other 207 emissions remaining after episodic emissions are identified. 208

Planned Emission (no abbreviation) – a release of gas which is part of the normal 209 operation of a facility as designed and can be either continuous or episodic. Examples 210 include: gas pneumatics, normally operating rod packing, unit or facility blowdowns, 211 liquid unloading, etc. 212

Unplanned Emission (no abbreviation) – a.k.a. “fugitive” emission – emissions from 213 locations which are designed to not emit, such as valve packing and pipe joints, etc. 214 While the GHGRP and GHGI utilize the term fugitive for these emissions, some fugitive 215 emission categories in the GHGI include some planned emission sources. 216

Fugitive Emissions (no abbreviation) – exactly the definition utilized by the GHGI 217 program, which includes some planned and all unplanned emissions sources normally 218 recognized at O&G facilities. 219

Activity data: 220

Activity Estimate (no abbreviation) – an estimate of the activity required for a stochastic 221 emission model. Activity can be in the form of counts (e.g. the number of pneumatic 222 controllers on a facility or the length of steel pipeline in a gathering system), time (e.g. 223 when a manual unloading occurred), rating or loading (e.g. load on a compressor 224 engine), or similar input data. 225

Activity Factor – mean of an activity estimate. 226

Partner Data (no abbreviation) – data, some of which may be provided under non-227 disclosure agreement, about partner facilities, pipelines or operations from either any 228 company that provided internal data. 229


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Other data sources: 230

GHGRP Activity / Emissions Estimate / Emissions Factor – input or output data taken 231 from published GHGRP data. When utilized, text will indicate the specific source of the 232 data. 233

GHGI Activity / Emissions Estimate / Emissions Factor – input or output data taken from 234 published GHGI reports. 235

Atmospheric terms: 236

Planetary Boundary Layer (PBL) – lowest part of the atmosphere where air motion is 237 influenced by friction. Its composition and dynamic are strongly influenced by Earth’s 238 surface processes. 239

PBL Height or Depth – thickness of the PBL. At any location, the PBL height is not 240 constant over time. The PBL is shallower at night and during colder days and typically 241 deeper during midday and afternoon (when solar heating peaks) and during the warm 242 season. The top of the PBL is usually marked by a temperature inversion, a change in 243 wind speed and/or direction and a strong vertical gradient in the mixing ratio of surface 244 emitted air pollutants. During the day and under well-mixed conditions, surface 245 emissions will mix vertically throughout the PBL within 20-30 minutes. 246

Well mixed PBL – during the day, the PBL mixes out vertically when there is sufficient 247 solar heating and turbulence. 248

Nocturnal PBL – at night the Earth’s surface and air above it cool down radiatively and a 249 temperature inversion (increasing temperature with altitude) most often will set-in 250 especially on clear nights. The nocturnal PBL is not well mixed. 251

Free troposphere – Layer of the lower atmosphere just above the PBL. Horizontal wind 252 speed and direction are usually more uniform and stronger in the free troposphere and 253 horizontal gradients in chemical composition for gases emitted at the surface are less 254 pronounced than in the PBL. 255

People: 256

Study Team – any combination of the research staff that were involved in the 257 measurements or analysis, including contractors. Study team could be a specific group 258 who made a measurement (e.g. an Aerodyne Research Inc. team doing a tracer 259 experiment – “the study team made tracer measurements”) or a combination of 260 multiple organizations (“the study team modeled XYZ using this method”). 261

Study Partners – the group of industry sponsors who provided financial support for the 262 project, including AGA as “one partner.” 263

Two partners (Southwestern Energy and XTO Energy) provided facility access directly, 264 and two AGA members (Kinder Morgan and CenterPoint Energy) provided facility access 265 through association with AGA. 266

Study Sponsors – all groups who provided financial support for the project. 267


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Data Partners – industry operators who provided data, but no facility access or funding 268 for the project. 269

For specific listings of companies involved in the study, see “List of Study Participants,” 270 below. 271


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Table of Contents 272

Contents 273

Abstract/Executive Summary ............................................................................................... iv 274 Acknowledgements .............................................................................................................. vi 275 Acronyms and Standard Terminology .................................................................................. vii 276 Table of Contents .................................................................................................................xii 277 Table of Figures ................................................................................................................... xiv 278 Table of Tables .................................................................................................................. xviii 279 List of Study Participants ..................................................................................................... xix 280 1 Background and study objectives .................................................................................. 1 281

1.1 Introduction...................................................................................................................... 1 282 1.2 Comparison to other basin-scale studies ......................................................................... 3 283 1.3 Overview of this report .................................................................................................... 4 284

2 Field measurements and emission estimates ................................................................ 5 285 2.1 Study design ..................................................................................................................... 5 286 2.2 Selection and definition of the study area ....................................................................... 8 287 2.3 Design of the field campaign .......................................................................................... 13 288

2.3.1 Review of accessible sites for measurement ...................................................................... 13 289 2.3.2 Clustered random sampling design .................................................................................... 14 290 2.3.3 Pre-screening of high-emitting facilities ............................................................................. 19 291 2.3.4 Gathering pipeline study design ......................................................................................... 22 292 2.3.5 Distribution field campaign ................................................................................................. 23 293

2.4 Sampling and measurement platforms .......................................................................... 23 294 2.4.1 Deployed instrumented platforms and summary of sampling accomplishments for 295 regional scale components ............................................................................................................... 23 296 2.4.2 Facility-scale measurement methods ................................................................................. 33 297 2.4.3 Methods for gathering pipelines and distribution systems ................................................ 34 298

2.5 Summary of field campaign ........................................................................................... 34 299 2.5.1 Well pad measurements ..................................................................................................... 34 300 2.5.2 Gathering station measurements ....................................................................................... 43 301 2.5.3 Gathering pipeline measurements ..................................................................................... 56 302 2.5.4 Distribution system measurements .................................................................................... 59 303

2.6 Emission estimation for facilities ................................................................................... 63 304 2.6.1 Facility estimation and comparisons for well pads ............................................................. 63 305 2.6.2 Facility estimation and comparisons for gathering stations ............................................... 66 306 2.6.3 Emission estimation for other facilities and methane emission sources ........................... 68 307

2.7 Emission estimation and attribution for the study area ................................................ 68 308 2.7.1 Aircraft Mass Balance ......................................................................................................... 68 309 2.7.2 Emissions Attribution .......................................................................................................... 82 310

3 Basin-Scale Modeling ................................................................................................ 100 311 3.1 Activity estimates utilized for basin-level analysis ....................................................... 100 312 3.2 Methods to compare study area emission estimates .................................................. 103 313 3.3 Ground Level Area Estimate and Aircraft Mass Balance Comparison ......................... 105 314


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4 Answers to Study Key Questions ............................................................................... 108 315 4.1 What portion of total basin methane emissions can be attributed to oil and gas 316 industry sources? ................................................................................................................... 108 317 4.2 What portion of total basin methane emissions from oil and gas operations is 318 contributed by large emission sources? ................................................................................ 108 319 4.3 What portion of total basin O&G emissions are contributed by episodic emission 320 sources as opposed to routine ones? .................................................................................... 110 321 4.4 What is the distribution of facility-scale emissions from wellsites and gathering 322 facilities? Does it exhibit a similar behavior to that observed in recent studies on wellsite 323 components and other supply chain facilities, with a skewed, “fat tail” distribution of 324 emissions? .............................................................................................................................. 110 325 4.5 How large are the uncertainties for both bottom-up and top-down estimates?" ...... 110 326

5 Study Conclusions ..................................................................................................... 112 327 5.1 Operational learnings ................................................................................................... 112 328 5.2 Emission measurement observations .......................................................................... 114 329 5.3 Other study publications .............................................................................................. 116 330

References ........................................................................................................................ 118 331 6 Annexes ................................................................................................................... 120 332 333

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Table of Figures 335

Figure 2-1: Overview of study design including comparison processes. ........................................ 6 336 Figure 2-2: Study area facilities operated by study partners. ...................................................... 10 337 Figure 2-3: Study area map showing flight box for mass balance on October 1, 2015. ............... 11 338 Figure 2-4: Study area production facilities. ................................................................................. 12 339 Figure 2-5: Distribution pipeline map for study area. .................................................................. 12 340 Figure 2-6: Study area facilities. .................................................................................................... 14 341 Figure 2-7: Example of wells clustered into a well pad. ............................................................... 16 342 Figure 2-8: Example of clusters from clustering algorithm. ......................................................... 17 343 Figure 2-9: Cluster boundaries utilized for the field campaign. ................................................... 18 344 Figure 2-10: Overview of pre-screening design. ........................................................................... 20 345 Figure 2-11: Team dispatch design for facility pre-screening. ...................................................... 21 346 Figure 2-12 Sections of gathering pipeline surveyed during field campaign. .............................. 22 347 Figure 2-13: Aircraft measurement platform. .............................................................................. 23 348 Figure 2-14: Study area map showing all flight tracks. ................................................................. 25 349 Figure 2-15: Map showing the location of all flask samples collected by aircraft. ...................... 26 350 Figure 2-16: NOAA Mobile Laboratory with air inlets protruding in the front (left). ................... 27 351 Figure 2-17: Photographs of project-funded instrumentation. .................................................... 27 352 Figure 2-18: Map showing tracks of the NOAA van for the 11 ground sampling days. ............... 29 353 Figure 2-19: NOAA discrete air sampling system package. .......................................................... 29 354 Figure 2-20: NOAA Global Monitoring Division MAGICC Greenhouse gas analysis system. ........ 30 355 Figure 2-21: Partially automated NOAA Global Monitoring Division Perseus Gas 356 Chromatography-Mass Spectrometer. ......................................................................................... 30 357 Figure 2-22: NOAA ESRL Physical Sciences Division 915 MHz wind profiler as deployed in the 358 field. .............................................................................................................................................. 32 359 Figure 2-23: Hourly vertical profile of 5-minute average horizontal wind speed and direction. . 32 360 Figure 2-24: Instrumented vans deployed for the study. ............................................................. 34 361 Figure 2-25: Map of production measurements. ......................................................................... 35 362 Figure 2-26: Comparison of study population and measured well pad size. ............................... 36 363 Figure 2-27: Production emission source count by facility ........................................................... 38 364 Figure 2-28: Correlation between count of sources and measured emissions on production 365 facilities ......................................................................................................................................... 38 366 Figure 2-29: Tank vent emissions measured at production facilities. .......................................... 39 367 Figure 2-30: Liquid level controllers measured at production facilities. ...................................... 40 368 Figure 2-31: Distribution of OTM 33A measurements ................................................................. 40 369 Figure 2-32: OTM 33A measurements made at well pads. .......................................................... 41 370 Figure 2-33: Measurements made at well pads using tracer flux method. ................................. 42 371 Figure 2-34: Cumulative measured emissions for tracer measurements at well pads, excluding 372 one manual unloading. ................................................................................................................. 43 373 Figure 2-35: Measured gathering and boosting facilities. ............................................................ 44 374 Figure 2-36: Example of typical gathering station road access. ................................................... 45 375 Figure 2-37: Test of representativeness for gathering stations. .................................................. 47 376


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Figure 2-38: Distribution of measurement locations for two key source categories at gathering 377 stations .......................................................................................................................................... 49 378 Figure 2-39: Gathering station measurements classified by exception type ............................... 50 379 Figure 2-40: Total measured emission by component category .................................................. 51 380 Figure 2-41: Measured pneumatic device emissions from gathering stations ............................ 52 381 Figure 2-42: Emission rate distributions for rod packing vents at gathering compressor stations382 ....................................................................................................................................................... 52 383 Figure 2-43: Emission rate distributions for liquid level controllers at gathering compressor 384 stations .......................................................................................................................................... 53 385 Figure 2-44: Number of measurements and observed, not measured, sources at gathering 386 compressor stations ...................................................................................................................... 54 387 Figure 2-45: Emission rate measured related to number of measurements for gathering stations388 ....................................................................................................................................................... 54 389 Figure 2-46: Distribution of total emission rate from onsite device measurements at gathering 390 compressor stations ...................................................................................................................... 55 391 Figure 2-47: Distribution of facility-level emission rates from gathering stations, as measured by 392 dual tracer release. ....................................................................................................................... 55 393 Figure 2-48: Distribution of facility-level emission rates from gathering stations, as measured by 394 dual tracer release, not including one facility that was undergoing maintenance. ..................... 56 395 Figure 2-49: Methane measurements at 56 pigging facilities. ..................................................... 57 396 Figure 2-50: Emission locations at pigging facilities. .................................................................... 58 397 Figure 2-51: Methane measurements at 39 block valve locations. ............................................. 58 398 Figure 2-52: Emission locations at block valve locations. ............................................................. 59 399 Figure 2-53 Methane measurements at distribution network facilities ...................................... 60 400 Figure 2-54 Emission facilities in the distribution network. ......................................................... 61 401 Figure 2-55 Methane measurements at 34 reported leaks. ......................................................... 62 402 Figure 2-56 Emission pipeline types. ............................................................................................ 62 403 Figure 2-57: Paired measurements performed at 58 at production pads. .................................. 65 404 Figure 2-58: Study onsite estimate and tracer facility estimate comparison at 24 gathering 405 stations. ......................................................................................................................................... 66 406 Figure 2-59: Study onsite estimate and aircraft facility estimate at 6 gathering stations. .......... 67 407 Figure 2-60: Schematic of the mass balance concept. ................................................................. 69 408 Figure 2-61: Sampling and measurement strategy for regional mass-balance method. ............. 71 409 Figure 2-62: Time series of horizontal wind speed and direction from near the surface to 3-4 km 410 above sea level. ............................................................................................................................. 73 411 Figure 2-63: Time series of horizontal wind speed and direction measured by the NOAA wind 412 profiler near Bee Branch, AR. ....................................................................................................... 74 413 Figure 2-64: October 1st flight track. ............................................................................................ 75 414 Figure 2-65: Observed temporal growth in the PBL near Bee Branch. ........................................ 75 415 Figure 2-66: Aircraft measurements of virtual potential temperature, water vapor, dry air mole 416 fractions of CO2 and methane during 4 vertical profiling spirals conducted on October 1st. ...... 76 417


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Figure 2-67: Methane dry air mole fraction measured with Picarro instrument along the aircraft 418 upwind transect (red line) and two downwind transects flown in the middle of the PBL. ......... 77 419 Figure 2-68: Similar to Figure 2-64 above for October 2nd flight ................................................ 79 420 Figure 2-69: Similar to Figure 2-67 for the October 2nd flight ..................................................... 79 421 Figure 2-70: Calculated emissions for aircraft mass balance. ...................................................... 81 422 Figure 2-71: Sensitivity analysis of emission estimate to background CH4 treatment. ............... 82 423 Figure 2-72: CH4 and C2H6 dry air mole fractions measured in the NOAA flasks collected by both 424 the NOAA van (green symbols) and the aircraft (blue symbols). ................................................. 85 425 Figure 2-73: Same plot as above but zoomed in. ......................................................................... 85 426 Figure 2-74: C3H8 versus C2H6 dry air mole fractions measured in the NOAA flasks collected by 427 the aircraft. ................................................................................................................................... 86 428 Figure 2-75: nC4H10 versus iC4H10 dry air mole fractions measured in the NOAA flasks collected 429 by the aircraft. The average nC4H10 –to- iC4H10 enhancement ratio is 2.1. ................................. 86 430 Figure 2-76: Time series of CH4 and C2H6 dry air mole fractions in-situ measurements from the 431 NOAA van while mobile. ............................................................................................................... 87 432 Figure 2-77: Time series of CH4 and C2H6 dry air mole fractions in-situ measurements from the 433 NOAA van while parked and stationary downwind of the site. ................................................... 87 434 Figure 2-78: Data from a methane plume fit with a line to obtain a slope of 1.3%. .................... 88 435 Figure 2-79: Scatterplot of C2H6 versus CH4 for the in-situ measurements with the Aerodyne 436 analyzer (black circles) and the NOAA flasks (red diamond) downwind of a well pad with 437 detectable emissions. ................................................................................................................... 89 438 Figure 2-80: Scatterplot of C2H6 versus CH4 for the in-situ measurements. ................................ 89 439 Figure 2-81: Comparison of in-situ and flask C2H6 to CH4 slopes in plumes from seven different 440 facilities in the study area. ............................................................................................................ 90 441 Figure 2-82: Location of ground level NG plume measurements. ............................................... 91 442 Figure 2-83: Probability distribution of C2H6 to CH4 ODR slopes for measured emission plumes 443 from 78 facilities in the study region. ........................................................................................... 91 444 Figure 2-84: Probability distributions of C2H6 to CH4 ODR slopes for measured emission plumes 445 from 78 facilities in the Western and Eastern halves of the study region. .................................. 92 446 Figure 2-85:The study area is divided into triangles by connecting locations where mobile 447 laboratory natural gas C2H6/CH4 ratios were obtained using Delaunay Triangulation. ............... 93 448 Figure 2-86: Triangulation of the Western portion of the study area (left) and Eastern portion 449 (right) with all observed NG plumes ratios. .................................................................................. 94 450 Figure 2-87: CH4 (red) and C2H6 (green) on the last downwind aircraft transect for the October 451 2nd, 2015 flight, as a function of longitude. .................................................................................. 95 452 Figure 2-88: A raster flight of the Western portion of the study area conducted on October 5, 453 2015 is used to obtain Western C2H6/CH4 area ratios. ................................................................... 96 454 Figure 2-89: CH4 and C2H6 measured during leg 9 of the October 5 flight. The black box 455 indicates the region of the study area in which CH4 is enhanced. ............................................... 97 456 Figure 2-90: An orthogonal distance regression fit to data from a raster conducted over the 457 Western portion of the study area yields a Western C2H6/CH4 ratio of 1.1 ± 0.02. ..................... 97 458 Figure 3-1: Spatial activity data for the ground-level area estimate of emissions ..................... 100 459


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Figure 3-2: Typical Gaussian plume model. ................................................................................ 104 460 Figure 3-3: Schematic of spatially shifting emissions to a location comparable with aircraft 461 methods. ..................................................................................................................................... 105 462 Figure 3-4: Aircraft Mass Balance and Ground Level Area Estimate on October 1, 2015. The 463 GLAE shown represents emissions occurring 1-2 pm CDT. ........................................................ 106 464 Figure 3-5: Aircraft Mass Balance and Ground Level Area Estimate on October 2, 2015. The 465 GLAE shown represents emissions occurring 2-3 pm CDT. ........................................................ 106 466 Figure 4-1: Throughput normalized emissions at gathering stations measured by downwind 467 tracer. .......................................................................................................................................... 109 468 469


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Table of Tables 470

Table 2-1 Summary of 15 flights conducted by Scientific Aviation and NOAA/CU between 471 September 21 and October 14, 2015. .......................................................................................... 25 472 Table 2-2: Summary of 11 drives conducted by NOAA/CU during Fayetteville field study 473 between September 23 and October 8, 2015. ............................................................................. 28 474 Table 2-3 Species analyzed on NOAA Perseus GC-MS instrument. Yellow color denotes analytes 475 recently added to NOAA GMD GC-MS target list. ........................................................................ 31 476 Table 2-4: Contemporaneous measurements at natural gas production facilities ...................... 35 477 Table 2-5: Production Facility Measurement Summary ............................................................... 37 478 Table 2-6. Gathering and boosting station measurements. ......................................................... 46 479 Table 2-7: Onsite device measurements at gathering compressor stations ................................ 47 480 Table 2-8: Location of observed / not measured sources at gathering compressor stations ...... 48 481 Table 2-9: Source of onsite measurement by equipment and component types ........................ 49 482 Table 2-10: Summary of emissions measured onsite at gathering stations................................. 51 483 Table 2-11: Statistically similar method comparisons at well pads .............................................. 64 484 Table 2-12: Tracer/SOE comparison for highest-emitting well pads ............................................ 64 485 Table 2-13: Cumulative facility-level emission rates at compared gatherings stations. .............. 68 486 Table 2-14: Regional mass balance implementation requirements. ............................................ 69 487 Table 2-15: Summary of measurements used for the mass-balance calculation ........................ 72 488 Table 2-16: Summary of the mean observed values for the key atmospheric variables in the 489 mass-balance equation for the October 1st flight analysis. ......................................................... 78 490 Table 2-17 Similar to Table 2-16 for October 2, 2015 .................................................................. 80 491 Table 2-18: Methane and ethane sources in the study area ........................................................ 83 492 Table 2-19: Average natural gas C2H6/CH4 ratio for each study area region as determined by 493 Delaunay triangulation followed by a Monte Carlo simulation. .................................................. 94 494 Table 2-20: Aircraft C2H6/CH4 ratios resulting from multiple PBL transects. ............................... 98 495 Table 2-21 : Values used in Equation 7 to apportion CH4 emissions in the Western portion to 496 natural gas and non-natural gas sources ...................................................................................... 98 497 Table 3-1: Ground level area estimate and aircraft mass balance results for October 1, 2015. 106 498 Table 3-2: Ground level area estimate and aircraft mass balance results for October 2, 2015. 106 499 Table 4-1:Summary of relative uncertainties for mass-balance equation variables derived from 500 observations (from airplane and wind profiler) and resulting relative uncertainties for emission 501 estimates from individual flight transect and the 2 transect average for each day. ................. 111 502 503


xix

List of Study Participants 504

Lead Institutions 505

Personnel list with team lead for each organization in italics. 506

Colorado School of Mines (CSM) 507

Dag Nummedal, Kathleen Smits 508

Project managers: Cynthia Howell, Stephanie Villano, Jeremy Boak 509

Colorado State University (CSU) 510

Daniel Zimmerle, Clay Bell, Timothy Vaughn, Cody Pickering 511

National Renewable Energy Laboratory (NREL) 512

Garvin Heath 513

National Oceanic and Atmospheric Administration (NOAA) 514

Russell Schnell, Allen White, Clark King, Timothy Coleman 515

University of Colorado (CU) 516

Gabrielle Pétron, Stefan Schwietzke, Ingrid Mielke-Maday, Eryka Thorley, Jonathan 517 Kofler, Phil Handley, Benjamin Miller, Laura Bianco 518

Partner Research Entities 519

AECOM 520

Matt Harrison 521

Aerodyne 522

Scott Herndon, Tara Yacovitch, Rob Roscioli 523

GHD 524

Tom Ferrara 525

Montana State University 526

Berk Knighton 527

UC Davis 528

Stephen Conley (also Scientific Aviation) and Ian Faloona 529

University of Wyoming 530

Shane Murphy, Robert Field, Anna Robertson, Jeff Soltis 531

Washington State University 532

Brian Lamb 533


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Technical Review Committee 534

Fiji George – Southwestern Energy 535

David Hiller - Colorado Energy Research Collaboratory 536

Gib Jersey – Consultant for XTO Energy, a subsidiary of ExxonMobil 537

Pam Lacey – American Gas Association 538

Richard Meyer – American Gas Association 539

Tecle Rufael – Chevron 540

Desikan Sundararajan – Statoil 541

Lead personnel from the lead organizations also participated in the technical review 542 committee as per the governance agreement. 543

Steering Committee 544

Vanessa Ryan – Chevron 545

Fiji George – Southwestern Energy 546

Paul Krishna – XTO Energy, a subsidiary of ExxonMobil 547

Desikan Sundararajan – Statoil 548

David Hiller – Colorado Energy Research Collaboratory 549

Pam Lacey – American Gas Association 550

Lead personnel from the lead organizations also participated in the steering committee 551 as per the governance agreement. 552

Science Advisory Panel 553

Dave Allen – University of Texas, Austin 554

Adam Brandt – Stanford 555

Dan Cooley – Colorado State University 556

Steve Hanna – Harvard 557

Ken Davis – Penn State 558

Study Sponsors 559

Financial Contributors 560

US Department of Energy / RPSEA 561

Colorado Energy Research Collaboratory (CERC) 562


xxi

American Gas Association (AGA) 563

Chevron 564

Southwestern Energy 565

Statoil 566

XTO Energy, a subsidiary of ExxonMobil 567

Industry participants who provided data and site access: 568

Southwestern Energy 569

XTO Energy, a subsidiary of ExxonMobil 570

Kinder Morgan 571

CenterPoint Energy 572

Enable Midstream Partners 573

Industry participants who provided data, but no site access: 574

BHP Billiton (data) 575

Other financial and in-kind support 576

Bee Branch school district 577

The National Oceanic and Atmospheric Administration provided in-kind support and 578 funding from the NOAA Climate Program Office grant NA14OAR4310142. 579

In-kind support through the National Science Foundation AirWaterGas Sustainability 580 Research Project (Cooperative Agreement No. CBET-1240584) 581


1

1 Background and study objectives

1.1 Introduction

This study aimed to develop independent estimates of methane emissions from a producing onshore natural gas systems play using contemporaneous, multi-scale, measurement approaches and to then compare the results for the purpose of reconciling the estimates derived from various methodologies, including consideration of uncertainty. Such comparison was applied at two scales: study-region and facility.

Multiple methods were used to measure methane emissions at the facility scale. Three methods were utilized that measure all emissions from a facility (hereafter “facility scale”) using methane flux carried downwind from the facility. These methods include dual tracer flux methods[1], EPA “Other Test Method 33-A” (OTM33-A)[2], and aircraft-based mass balance method [3]. In addition, detectable point sources at the facility were measured using industry standard on-site measurement techniques, similar to those utilized in recent studies and EPA’s Greenhouse Gas Reporting Program (GHGRP). On-site measurements were complimented by engineering estimates of emissions from identified emission sources which could not be measured during the field campaign. Where possible, multiple methods were utilized simultaneously to support facility-level comparisons between methods.

Study-region scale methane emissions estimates were derived using boundary layer airborne measurements upwind and downwind of the study region in a regional mass balance model [4]–[7]. Additional measurements and modeling supported attribution of atmospheric methane emissions to the oil and gas (O&G) and non-O&G sectors.

The flight boundaries of airborne measurements define the portion of the production basin, termed here as “study area”, utilized to compare a ground-level area estimate (GLAE) of emissions to airborne mass-balance estimates. Some consider the GLAE to represent a ‘bottom-up’ estimate of emissions; others consider only direct emission rate measurements at the source or engineering calculations used in emissions inventories to represent the ‘bottom-up’ estimate and any downwind measurements at the ground or in the air as being ‘top-down’ measurements. Since the terms bottom-up and top-down are ambiguous in previous studies, these terms are not used in this work and, instead, more descriptive terms will be used as described later. To create the GLAE, statistical methods were utilized to scale facility-level measurements to encompass all sources in the study area. To minimize the impact of operational differences on the comparison, all emission estimates are time-resolved to reflect O&G operations that occurred at the time of comparison, provided time-resolved activity data was available for this purpose. Where possible, measurements were made concurrently to minimize the impact of changing operations or episodic events that could potentially affect comparisons as well as emissions quantification.


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Key questions considered by this study include:

Estimate comparison, or estimate “reconciliation,” questions: o Will making measurements simultaneously at the device-, facility-, and

area-scale assist in estimating and understanding the persistent differences seen in previous studies comparing aircraft mass-balance and bottom-up approaches using inventory and emission factor methods?

o Are there significant differences between estimates from multiple methods at the facility scale?

o Do facility level methods measure all emission sources? o How large are the uncertainties for both aircraft and facility estimates?

Will better quantification of these uncertainties explain previously noted reconciliation issues?

Source attribution questions: o What portion of total basin natural gas emissions are contributed by

episodic emission sources as opposed to routine ones? o What is the temporal variability in emissions within the study area? o What portion of total area methane emissions can be attributed to

natural gas industry sources in a basin? o Do estimates of non-O&G methane sources made from available data

reconcile with measurements made in the basin? o How are O&G methane emissions distributed between different source

categories and different source sizes?

Methodological questions: o Can simultaneous measurements by multiple techniques at a single

facility highlight potential methodological or protocol issues with emissions quantification methods and reduce the uncertainty?

o How do skewed emissions distributions and large episodic emissions impact the comparison of methods at the facility-scale and area-scale?

o How does site access and access to concurrent operations data from operators impact the results and reduce uncertainty?

o What measurement method or methods are most appropriate to allocate emissions between O&G and non-O&G?

The analysis and comparison of the emission estimates made at the facility and study area scales has led to an improved understanding of the accuracy, strengths and limitations of the implemented measurement and modeling approaches. Based on this, we provide an objective interpretation of the study data and insights on how our results add to the current and expanding scientific and technical literature on natural gas systems methane emissions.


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1.2 Comparison to other basin-scale studies

Almost all prior regional methane emission studies have compared temporally confined, measurement-based, top-down, estimates of emissions with bottom-up estimates based upon reported or inventory-based activity estimates and published emission factors. Activity estimates are typically based upon annual average data or annual counts. Emission factors are typically derived from measurements or survey data in multiple basins over an extended period. [8]

A recent study of emissions in in the Barnett shale play, coordinated by the Environmental Defense Fund (hereafter “Barnett Coordinated Campaign”), also investigated methane emissions at the facility and basin scales over several time windows that spanned from March to October 2013.[7], [9], [10] Top-down area emissions were estimated utilizing aircraft mass balance methods, similar to those utilized in this study. Bottom-up estimates were driven by a combination of measurements conducted during the campaign, activity data from public sources, and emissions factors from a variety of published material. With the exception of gathering compressor stations, all bottom-up measurements were conducted without the cooperation of operators in the Barnett region, and measurement locations were allocated using a variety of methods, but generally did not produce statistically random samples of facilities in the area.

This study utilized different methods deployed contemporaneously to improve both the fidelity of measurement results and the temporal and spatial specificity of emission estimate comparisons, including:

1) Pre-planned random sampling of partner facilities prior field campaign, supported by industry partner data;

2) Accumulating activity data for the overwhelming majority of facilities in the study area from industry partners;

3) Completing independent facility scale measurements using multiple methods simultaneously;

4) Direct, ground-level (on-site or downwind) measurement of every natural gas sector in the study area, including production, gathering, transmission and distribution. (There are no processing plants or storage facilities in the area.);

5) On-site access to facilities, supported by industry partners, during all measurements and daily logs of all activities in the study area from study industry partners;

6) Mass-balance downwind aircraft flights and dense raster flights over the study area with fast response methane and ethane measurements;

7) Ground level surveys to map methane and ethane emission ratios for multiple methane sources in the study area.


4

8) Completing temporally and spatially resolved models of emissions to compare facility-scale and area-scale contemporaneous emission measurements.

1.3 Overview of this report

The remainder of this report is divided into three chapters. Chapter 2 describes the study method and reviews the number and mix of measurements completed by all measurement teams. The field campaign is covered in the first five sections, including the study design (2.1), the study area (2.2), overall field campaign design (2.3), measurement methods (2.4) and a summary of the field campaign measurements (2.5). Remaining sections describe modeling efforts utilized for both facility scale (2.6) and basin-scale (2.7) estimates. Chapter 3 described the modeling method utilized for the bottom-up model. Chapter 5 describes results from the study, and other publications created during the study.

Throughout, reference is made to annexes containing protocol documents, data tables and associated supporting information. Reference is also made to published papers, which may add additional insight or analysis to this report.


5

2 Field measurements and emission estimates

The work presented here consists of three distinct phases. First, a field measurement campaign was conducted to acquire contemporaneous, multi-scale, measurements of facilities in a mid-continent gas-producing basin, the Fayetteville Shale play of the Arkoma basin, Arkansas. Second, these data were utilized to develop robust, comparable estimates of emissions at the facility scale for facilities that were measured utilizing multiple methods. Comparison of multiple methods at a representative sample of facilities highlights the strengths and weaknesses of measurement methods and provides more robust estimates of facility emissions, by facility type. Finally, facility-scale emissions estimates are developed into an area-scale estimate of emissions, which can be compared with mass-balance estimates of emissions made by aircraft.

This chapter describes the field measurements campaign, including the design and an overview of the sampling accomplished during the campaign, and key methods utilized to estimate emissions and scale those estimates to the study area. For efficiency, descriptions of study design and protocols are combined with summaries of the campaign results for each measurement method.

Section 2.1 provides an overview of the study design and methods to compare emission estimates. Section 2.2 discusses the critical element of study area selection. Section 2.3 details the design of the field campaign, including timing and sampling strategy. Section 2.4 discusses each of the sampling and measurement platforms. For efficiency, this section also summarizes the sampling completed by each team during the field campaign. Section 2.5 describes methods utilized to estimate emissions at the facility level, while Section 2.7 provides the same information for the area level methods. Section 2.7.2.1 describes how facility level measurements and area level activity data were utilized to scale facility level estimates the area estimates comparable with aircraft mass balance estimates. Finally, Section 3.2 describes methods to compare area level estimates.

2.1 Study design

This study was designed to emphasize the comparison of methods in preference to using fewer methods and measuring more facilities. As a result, measurement teams were dispatched during the field campaign to measure, as nearly simultaneously as possible, randomly selected locations. Each pair of measurements at a location is an experiment comparing independent emission estimates while minimizing differences between the conditions being measured. Not all attempts at paired measurements were successful, but a significant number of locations were independently measured by two, and in some cases three, methods.

The field campaign was designed prior to the in-field measurement campaign. The design approach focused on the principles to be applied to the field campaign (e.g.


6

methods for clustering facilities and random sampling) to allow the study team leadership to adapt the campaign for weather conditions, facility accessibility, and other unforeseen constraints. To facilitate the design, key members of the study team visited the study area in August 2015 to familiarize the team with the facilities in the basin and to select, in cooperation with study partners, locations for staging equipment.

Figure 2-1 provides an overview of measurement and estimation methods in the study, and emphasizes comparisons between methods. Acronyms for each of the major estimates are shown in italic bold.

Figure 2-1: Overview of study design including comparison processes. The study was divided into six sets of activities (color-coding) including both measurement methods and subsequent analysis processes. Abbreviations for major estimate sets are indicated by bold italics in the appropriate boxes.

The study design contained six “activity sets,” building toward the comparison and reconciliation of study area estimates of emissions. They are:

1) Estimate of facility level emissions using on-site measurements. Onsite measurements utilized industry-standard methods to measure emissions at the device level (Onsite device measurements, or ODM). Engineering estimates were utilized to estimate emissions for sources which could not be measured due to safety or resource constraints. The activity data was provided by industry partners and/or other publicly available resources. Measurements and engineering estimates were combined to produce a Study Onsite Estimate (SOE)

Activity Data

Study On-Site EstimateSOE

(per facility)

On-site Device Measurements

ODM

Engineering Estimates for Unmeasured

OTM33A Facility

MeasurementsOFE

Ground-Level Area Estimate (GLAE)

Aircraft Mass Balance

Measurements

Facility Comparison Study Area Comparison

Airplane Facility

MeasurementsAFE

Study Area Emissions

Comparison

Aircraft Area Estimate

AFE

Source Attribution Measurements

Dual Tracer Flux Facility

MeasurementsTFE

Facility Level Comparisons

Legend

Onsite Ground-level scale up

Facility downwind estimates Aircraft area estimate

Area level comparisonFacility level comparison


7

for each measured facility which best estimates the total methane emissions for each measured facility. For the purposes of this study, gathering pipelines and auxiliary equipment along the pipeline are considered a facility.

2) Estimate of facility level emissions using methane entrained in wind passing over and through the facility, commonly referred to as “downwind methods.” Measurements were completed utilizing dual tracer flux (TFE), OTM33A (OFE), and aircraft spiral flights (AFE).

3) Comparison of facility level emission estimates. These are fundamentally pair-wise measurements of two emission estimates of the one facility in the same operating state. In most cases, these measurements were made concurrently or back-to-back. In a few cases, measurements were made hours apart, but in the same operating state.

4) Estimate of study area emissions by aircraft mass balance methods, including attribution of total emissions to source categories.

5) The scale-up of facility level emissions to a study area emission estimate (ground level area estimate, or GLAE). The GLAE makes extensive use of activity data from study partners and public sources. The GLAEs are also time-resolved to match the timing (midday-afternoon hours) of mass-balance measurements made by aircraft.

6) Comparison between area level estimates. The research team performed pair-wise comparisons of area estimates, which included, where possible, analysis of spatial distribution of the emissions.

Field measurements were completed during a five week field campaign in September-October 2015. Not all measurement teams were present for all five weeks of the campaign. Site access was provided by local operators and was managed by Colorado State University (CSU); facility measurements were observed by either CSU or members of at least one of the measurement teams. Scientific Aviation flights and NOAA wind profiler and mobile ground measurements were coordinated by CIRES scientists who also had daily morning briefings with the local National Weather Service office in Little Rock, AR. Further descriptions of site access and measurement quality control are provided in study publications.


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2.2 Selection and definition of the study area

As indicated above, an important goal of the study was to compare ground-level methods – both downwind and onsite methods – with aircraft mass balance measurements of total methane flux in the study area and the estimates of the fraction of total emissions from natural gas operations.

In an earlier stage of the project, measurements were made in the DJ basin in Colorado, without the benefit of access to facilities or activity data from operators in the basin. During this work, development of aircraft mass-balance estimates proved difficult due to problematic weather conditions, and development of a ground-level emission estimate was hampered by both the lack of site access during measurements and lack of activity data for the scale-up to a GLAE. Results for these early phases are available through the project repository at the Department of Energy and at the RPSEA web site at Colorado State University. [11]

For the work described here, the study leadership decided three ingredients were necessary to support a strong response to the study’s objectives:

1) Access to a significant portion of study area facilities to develop a strong ground-level measurement approach;

2) Access to detailed activity data for the majority of production and gathering operations in the basin – and ideally – all other O&G operations in the basin concurrent with aircraft measurements;

3) Identification of a reasonably compact and isolated study area that supported clearly-defined flight boundaries, and had a strong probability of suitable winds during the fall of 2015.

While operators other than the study partners may have been recruited for site access and because of the experience in the DJ basin, it was decided for both time and resource reasons to identify a basin where study partners operated a significant portion of the facilities in the basin.

Since study resources were limited, it was also important to limit the diversity of source categories, which needed to be characterized to develop a complete estimate of emissions. In wet gas basins, gas is typically upgraded in gas processing plants, which have been difficult to characterize without extensive measurement [12], [13]. Heavily urbanized areas require more extensive measurement of distribution infrastructure than rural areas, and have more diverse and distributed sources of methane releases, both biogenic and anthropogenic. [9] While these sources are interesting in themselves, they were not essential to the goals of the study and would have added significant complexity to a limited field campaign.


9

Given the above factors, it was noted that two study partners, XTO Energy and Southwestern Energy, operated a significant fraction of production and gathering within the eastern Fayetteville shale play. Natural gas produced in this area is “sweet and dry,” and requires minimal gas upgrading to achieve to pipeline quality. As a result, there are no gas processing plants and little treating is required. Wells in the play produce water, which is separated from the gas at the well pads utilizing gravity-type separators. Gas is further dehydrated at the gathering compressor stations using glycol dehydrators. These factors limit both the diversity of facilities and the diversity of location of facilities in the study area.

In addition, the American Gas Association and CSU coordinated access to transmission facilities (Enable Midstream Partners and Kinder Morgan) and distribution facilities (CenterPoint Energy) in the study area. With this combination of partners, the study team had access to approximately ¾ of the O&G facilities in the basin, from production through distribution.

All of the above companies provided extensive, confidential, data about their operations to the study team. They provided essential data prior to the campaign to assist in pre-campaign planning. Post-campaign, companies provided specific activity data for their operations, including records of episodic events which occurred during the campaign. Finally, post-campaign, the remaining major production and gathering operator in the basin – BHP Billiton – provided activity data for their operations in the study area. Activity data will be described in more detail in Section 2.7.2.1.

Figure 2-2 summarizes facility count data and provides an indication of the total number of facilities in the region.


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Figure 2-2: Study area facilities operated by study partners.

Several other aspects of the study area are of interest:

There are no active gas producing basins in the immediate vicinity to the north, east or south of the study area, reducing the complexity of up-wind methane emissions when wind is from any of these directions. There is a relative “gap” in wells in Pope County, to the west of the study area, providing a convenient location for the western edge of the study area.

There are few urban areas and few high-emitting non-O&G sources (e.g. landfills) in the study area.

Agricultural operations include chickens, cattle, and rice cultivation – all of which have non-trivial biogenic methane emissions.

CenterPoint owns all gas distribution in the study area.

Figure 2-3 provides a map of the study area, located in Cleburne, Conway, Faulkner, Independence, Jackson, Van Buren, and White counties, and includes a narrow strip on the eastern side of Pope County, as indicated by the flight box flown by the aircraft during mass balance flights. The region extends approximately 65km (40 miles) in the N-S direction and 150km (93 miles) in the W-E direction. The boundary of the Fayetteville Shale Play (light blue in figure), is taken from Energy Information data. Figure 2-4 shows all known wells in the study area, and includes wells in Pope County, to the west of the study area, for reference.

Asset Type

Access and

Activity Data

Activity Data

Only

Non-Partner

Operated

Study Region

Total

Access

Fraction

Data

Fraction

Active Natural Gas Well 4554 938 50 5542 82% 99%

Active Oil Well 0

Gathering & Boosting Stn1 95 33 0 128 74% 100%

Transmission Comp. Stn 8 0 3 11 73% 73%

Notes:

1) Total gathering station counts from public data are imprecise; additional operators may exist.


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Figure 2-3: Study area map showing flight box for mass balance on October 1, 2015. Light blue outline is the Fayetteville Shale Plan (Energy Information Administration5). The flight box is defined by the north, east, south, and west flight lines, as indicated. Spirals indicated on the map are used to identify the top of the boundary layer during measurement.

Public data from the Arkansas Oil and Gas Commission was utilized to identify active oil and gas wells within the study region. Gathering station data, pulled from public State Environmental Quality websites, was processed by CSU [12] as part of another study to identify gathering stations from other reported facility types. Transmission station data was pulled from the EPA’s Greenhouse Gas Reporting Program (GHGRP) and from additional analysis during another CSU study of the transmission and storage sector [14]. Distribution pipelines within the study area, shown in Figure 2-5, are concentrated in peri-urban areas, primarily small towns and some corridor facilities.

5 http://open.fedmaps.opendata.arcgis.com/

Little Rock Area

Flight Boundary

Fayetteville Shale PlayFlight Spirals

http://open.fedmaps.opendata.arcgis.com/


12

Figure 2-4: Study area production facilities. The Fayetteville shale play is shown in light blue. Partner well locations are shown in red, non-partner in green. Data from Arkansas Oil & Gas Commission.

Figure 2-5: Distribution pipeline map for study area. Map image courtesy of CenterPoint Energy.


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2.3 Design of the field campaign

This section discusses the design and execution of the field campaign, which happened over a five-week period from late September to early October, 2015.

2.3.1 Review of accessible sites for measurement

As indicated above, Southwestern Energy and XTO Energy provided access to production well pads, gathering pipelines, and gathering compressor stations (often called “gathering and boosting” stations). Enable Midstream Partners and Kinder Morgan provided access to transmission compressor stations in the study area. CenterPoint provided access to distribution systems. With the cooperation of CenterPoint and Enable, the study team had access to both the transmission and distribution side of the “city gate” stations – the transfer and sales point between the transmission and distribution systems.

Figure 2-6 illustrates the well locations in the study area in relationship to non-gathering pipelines in the area, both superimposed over black rectangle approximating the study area. Pipeline locations are important to understand ethane/methane ratios in portions of the basin. The production and gathering facilities shown in red were accessible for measurement during the study.


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Figure 2-6: Study area facilities. The pipeline corridor shaded in blue contains several pipelines, including transmission natural gas lines, refined products, and at least one LPG/NGL pipeline. All facility locations shown are from public sources and were extracted prior to June 2015. Pipeline data is from a 2014 MapSearch™ database, and is likely current for infrastructure in 2013.

2.3.2 Clustered random sampling design

This section provides an overview of the randomized sampling design of the study. The protocol utilized during the campaign is included in Annex 1: Screening and Guided Measurement Protocol.

To meet the study objective, it was necessary to (a) perform as many paired, contemporaneous measurements as possible at production and gathering facilities, and (b) accurately document operations at those facilities while measurements were being made. Since the measurement campaign was limited in duration, travel time between facilities presents a significant time penalty and reduces the number measurements performed. CSU’s past experience from other studies [1], [12]–[14] indicated that clustered random sampling methods provided statistically valid samples while minimizing travel time between measurements. Therefore, the field campaign for this


15

project was structured similarly to the CSU’s experience in a previous gathering and processing (G&P) study6.

Since well pads, not wells, constitute a measurement target for this study, wells were grouped into well pads by combining all wells within 100 m of each other into a well pad. Figure 2-7 illustrates the results of such a grouping. While this method may err in some instances, an audit of its efficacy found zero problems.

Pre-campaign, measurement resources were allocated as follows:

Six days for one tracer team, from Aerodyne research, were allocated to transmission stations. Days were planned about two weeks in advance to arrange for appropriate site access to these facilities. Transmission stations were measured using only downwind tracer methods. Prior work [14] by the team has shown that downwind and onsite measurements at transmission stations agree sufficiently that only one method is required.

Both tracer teams were also assigned to pre-screen facilities during the first week to guide measurements during the project. This pre-screening effort, described below, was unsuccessful due to road access downwind of the facilities, and, to a lesser extent, meteorological conditions during the campaign.

One measurement team, from GHD, was assigned all distribution and gathering pipeline infrastructure. Three weeks were allocated for gathering pipelines, and the remainder to distribution. However, in execution, the GHD team was able to field two units, and performed three weeks of gathering pipelines, and approximately four weeks of distribution infrastructure, with some interruptions when all of the team was needed at one facility or pipeline location.

The top priority for aircraft measurement time was allocated, in priority order, to (1) mass-balance measurements of the study area, (2) raster flights – closely spaced transects of the study area – to perform source attribution modeling, and (3) measurement of both O&G and non-O&G facilities – primarily landfills, gathering stations and transmission stations. Supervising staff dispatched aircraft to facilities being measured by onsite and downwind teams whenever possible as part of this third priority.

The remaining onsite and downwind measurement teams were allocated to perform random measurements of well pads and gathering stations, using clustered random sampling for four weeks of the measurement period.

Configuration of all measurement teams is discussed in Section 2.4.

6 http://energy.colostate.edu/p/gathering-and-processing-plants

http://energy.colostate.edu/p/gathering-and-processing-plants


16

Figure 2-7: Example of wells clustered into a well pad. GPS coordinates of wells are shown as red dots. While the alignment with satellite imaging is not perfect, most well locations were within 10 meters of the satellite images. The well pad center, computed from well GPS coordinates, is indicated by a yellow dot.

Facility clusters were created by randomly selecting a gathering station as starting point and grouping the 100 nearest partner well pads into a cluster around that gathering station. Gathering stations within the outer boundary of these grouped well pads were also added to the cluster. The clustering algorithm then selected the next gathering station in sequence, until all well pads have been assigned to a cluster. The result included both dense, central, clusters and perimeter clusters which contained well pads which were located on the edge of the play and not proximate to other well pads. Figure 2-8 illustrates the result of one run of the clustering algorithm.


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Figure 2-8: Example of clusters from clustering algorithm. Well pads in each automatically generated cluster are assigned a randomly selected pin color. Centrally located clusters tend to be compact and relatively small. Peripheral assets were assigned “to the last cluster” and are spread over a larger area.

The algorithm was executed repeatedly, starting from different randomly selected points – i.e. gathering stations. Cluster patterns were graded for representativeness, and one run of the clustering algorithm was selected that had representative clusters across the east-west and north-south range of the study area. Clusters were then selected from western, central (east-west), and eastern portions of the study area. Six total clusters were selected, in three pairs, as shown in Figure 2-9. Each pair was treated as a single cluster during the field campaign.

Note that clusters were utilized only for well pads and gathering station sampling. A separate randomized selection was performed for gathering pipelines and distribution infrastructure (see below), and all accessible and operating transmission stations were measured for the study.


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Figure 2-9: Cluster boundaries utilized for the field campaign. Clusters are shown as red or blue shapes, defined as the outer boundary of the well pads selected to be in the cluster. Red or blue coloring has no practical significance, and is only utilized to show individual cluster groups within the east, central, and western study clusters. Well and gas gathering locations shown as yellow dots, study area is large black square.

Pre-planning provided a basic design of the measurement campaign, which was tactically guided daily by supervising staff from CSU. Facility selections were updated each evening for the following day’s measurements. The selection was conditioned based upon:

1) Wind direction: For a given wind direction, a subset of facilities have roads downwind that are at a suitable distance for tracer measurements. (See annexes). When tracer teams were available for either gathering stations or well pad measurements, facilities were sorted by how suitable they were for measurement based upon wind direction.

2) During the measurement campaign, supervising personnel checked the mix of facilities measured, and guided measurements toward under-represented populations.

3) Within the clusters, measurements were grouped so that all facilities measured in a particular day were close together to minimize drive time. This was done by:

a. Identifying unmeasured facilities with proper road access for the wind conditions.

b. Randomly selecting one of these facilities. c. Measuring that facility. d. Moving to additional facilities that had suitable wind conditions, in order

of distance from the first facility.


19

This approach minimized the drive time between measurements, and allowed auxiliary resources, such as the man lift for measuring elevated sources at gathering stations to be readily moved between facilities.

4) To ease scheduling of partner resources, each of the operating companies was assigned days of each week when sampling would be done on their facilities. Days were assigned proportionately to the number of well pads each company operated in the basin.

2.3.3 Pre-screening of high-emitting facilities

Almost all prior studies of natural gas emissions have indicated that a few sources or facilities are responsible for a disproportionate share of total measured emissions with their respective study design area.7 In campaigns using random sampling, the number of measurements made for these high emitting facilities tends to be low: Since some of these sources are episodic in nature and total populations are small, prior measurement campaigns may have under-represented these high emitters in their sample population during the measurements. Given that emissions in this study were likely to have a similar skewed distribution, the design of this study attempted to compensate for this effect by pre-screening facilities, detecting high-emitting facilities, and performing more measurements on high-emitting facilities. This type of over-sampling of large emitters would provide more measurements in a critical portion of the emissions distribution. Pre-screening was further developed during a visit to the field locations in August of 2015. As discussed later, this approach ultimately was not used due to adverse logistical conditions that occurred in the field.

An overview of the pre-screening method is shown in Figure 2-10. Production clusters (3 cluster in this example, each with 100 well pads) are targeted for measurement. A fast screening method screens a large proportion of the facilities (240 or 300 possible in this example). These are classified on a qualitative scale of 1-5, with 5 being the highest emitting facilities. Approximately 2/3 of the measurements are targeted at the highest-emitting facilities (#4 and #5 classifications). In the example, it is estimated that measurement could occur at 45 facilities, 30 high-emitting facilities and 15 low-emitting facilities. Results of these measurements are combined, into a single emission distribution by weighting each measurement by the number of facilities in “screened pads” that each measurement represents.

The screening method developed for this process was a modified downwind transect approach. Mobile measurement teams from both Aerodyne and University of Wyoming would sample methane concentrations downwind of facilities, and utilizing simple back-propagation models, estimate to within ±100%, the emissions from screen facilities.

7 The skewed distribution of emissions is also colloquially known as the “long tail” or “fat tail” distribution of emissions, after the shape of the probability distribution function of the measurements.


20

This qualitative evaluation would identify higher-emitting facilities while using only a small fraction of the measurement resources.

The advantage of this method is that is provides more measurements and superior characterization of high-emitting facilities, which drive total emissions within a population of facilities. The method is highly dependent upon screening a large number of facilities within a short period of time; initial projections indicated that it would be possible to screen ½ to ¾ of a cluster within one day, using two downwind teams. A representative sample of the team dispatch plan is provided in Figure 2-11.

Figure 2-10: Overview of pre-screening design.

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CDF “Big”

CDF “Small”

Production Clusters

Screened Pads

Measured Pads

Scaled Sample

Production CDF

CDF “All Screened”60 sites

180 sites


21

Figure 2-11: Team dispatch design for facility pre-screening.

During the measurement campaign, even though conditions had been evaluated during August, field conditions during September did not support the screening effort for two reasons:

1) Most facilities were ½ km or more off public roads. An escort was required to access nearby roads to make drive-by screening measurements. Coordinating access proved complex and time consuming. For the downwind team utilizing OTM 33A methods (University of Wyoming), screening time approached that of making a complete OTM 33A measurement. Teams were unable to achieve sufficient site count for screening to meet design objectives. Figure 2-36 provides some illustrative examples of facility locations and access roads.

2) The wind was insufficiently strong, given the level of emissions, to produce a defined plume sufficiently downwind to be assessed from public roads, or nearby non-public roads accessible to study partners. When public roads were not available, a single road typically approaches a well pad or gathering facility. If the road was not configured so that wind (a) came from the facility, and (b) crossed the approach road at approximately a right angle, the screening method could not be completed.

The screening method was attempted for the first week of the measurement campaign, and then discontinued when it proved ineffective. The study continued with the clustered random sample, described earlier. Given the effectiveness of the on-site and downwind teams, the clustered sample provided excellent and representative measurement across the basin.


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Given the time and location of the measurement campaign, the road network, wind conditions, and vegetation cover made the screening method unworkable. However, the same technique could potentially be successful in a basin with an open, grid-like, road system during a period of higher winds. Good candidates for such work would be the DJ Basin in Colorado, Bakken in North Dakota/Montana, or similarly open basins in central and western USA. The method is not recommended for hilly and forested areas with non-uniform road systems, such as the Marcellus Shale, basins in Louisiana, and, of course, Arkansas.

2.3.4 Gathering pipeline study design

Emissions measurements were made on a small, random, sample of gathering pipelines in the study area. In addition to the underground pipelines themselves, measurement teams also measured above-ground auxiliary equipment, including pig launchers, pig receivers and block valves. Planning of the pipeline campaign is detailed in Annex 10, and the measured pipeline segments are summarized in Figure 2-12.

Figure 2-12 Sections of gathering pipeline surveyed during field campaign. Highlighted pipeline segments are driven ROWs. Purple dots are individual well pads, orange line is flight boundary for the October 1st mass balance flight. Measurements were biased toward the western half of the study area because one partner mows only half of their ROWs each year, and in 2015 had mowed more in the western half of the study area. Qualitatively, pipelines across the study area are configured similarly, and there is no evidence that emissions behavior would be statistically different on western or eastern halves. Image provided by Google Earth.


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2.3.5 Distribution field campaign

Since all distribution in the study area was operated by one company – an AGA member and study partner – the study team had complete access to the distribution facilities. The measurement campaign utilized methods developed by Lamb, et. al. [15] for a recent distribution measurement study. Study design and methods are detailed in Annex 11.

2.4 Sampling and measurement platforms

2.4.1 Deployed instrumented platforms and summary of sampling accomplishments for regional scale components

2.4.1.1 Aircraft

A Mooney Ovation single engine airplane contracted from Scientific Aviation completed 15 flights between September 21, 2015 and October 14, 2015. (Figure 2-13) The instrumentation onboard the aircraft includes:

Wavelength scanned cavity ring-down analyzer for methane, carbon dioxide and

water vapor (Picarro 2301-f) operating at 0.5Hz.

Quantum cascade laser ethane analyzer (Aerodyne) with 1Hz measurements.

Discrete air sampling with NOAA aircraft programmable flasks package (PFPs)

system and analysis at NOAA for over 50 chemical species including C1-C6

straight chain alkanes, and several combustion markers such as carbon

monoxide, acetylene and carbon dioxide.

GPS location and time

Temperature

Instantaneous horizontal wind speed and direction at location of aircraft

Figure 2-13: Aircraft measurement platform. Photographs of the SA Ovation Mooney aircraft (left) and the instrumentation in the back of the airplane (right). The data is streamed in real time to a display for the co-pilot to see and online with a short delay for collaborators to assist in real-time data analysis and decision-making.


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The aircraft Picarro instrument was calibrated at NOAA after the field deployment using

4 calibrated whole air tanks prepared by NOAA GMD’s Calibration Lab and ranging from

1.774 to 3.060 ppm CH4. Air from a non-calibrated air tank was also analyzed daily post

flight to check for potential drifts in measured values. Results from the in-field and lab

tests were used to post-process and report the Picarro data on the WMO/NOAA

methane and CO2 calibration scales.

A different Mooney aircraft from Scientific Aviation with the same instrumentation and

data handling was operated in previous regional mass balance studies conducted in

collaboration with NOAA, CU and UC Davis [5]–[7].

All flight tracks are shown in Figure 2-14. Four flights were flown in the mass balance

“box” pattern in the planetary boundary layer (PBL) upwind and downwind of the entire

study region. The goal of the mass balance flights was to collect measurements to

estimate total methane emissions from the study region. 1-3 vertical profiles were also

conducted during mass-balance flights to determine the planetary boundary layer) PBL

height on the upwind and downwind transects.

Eleven flights were flown in a raster pattern in the surface boundary layer over part or

the entirety of the study region to investigate ethane and methane correlation patterns

to constrain a measurement-based source attribution model for the study region

methane emissions.

During several of the raster flights, individual facilities were targeted by the airplane

with spiral pattern flights to quantify facility-level methane emissions (see further

details in next section). These facility-level emission mass-balance spiral flights were

conducted in coordination with the ground teams to ensure contemporaneous

measurements. One flight was dedicated to investigate methane emissions from four

landfills in the vicinity of Little Rock.

All flights took place during midday, when the planetary boundary layer (PBL) was well

mixed vertically and close to fully-grown, and lasted 5-6 hours each, during which time

the airplane covered ~800 km (~500 miles).


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Figure 2-14: Study area map showing all flight tracks.

The aircraft team’s main priority for flight planning and execution was to take advantage

of days with the best meteorological conditions for regional mass-balance emission

quantification (as opposed to raster flights). These conditions include:

Well-mixed fully-grown PBL (flight start ~ 11am)

Consistent winds throughout the PBL and over the study region (no major

change in wind direction for example). Ideal wind speeds in the PBL are in the

range 4-10m/s.

No recirculation of an old air mass

No large-scale convection, which would tend to pump surface air impacted by

local emissions high up above the PBL

No thunderstorms

Table 2-1 Summary of 15 flights conducted by Scientific Aviation and NOAA/CU between September 21 and October 14, 2015.

Flight Pattern/Target Dates Notes

Successful study region

Mass-Balance (MB)

October 1, 2015

October 2, 2015

Strong steady winds (7m/s) from N

or NNE on both days

Box pattern, no MB September 21 and 23, 2015

Low (~ 2-3m/s) variable winds,

with nocturnal emissions pooling

September 26, 2015 Partly cloudy, NE wind ~ 5m/s


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Flight Pattern/Target Dates Notes

Entire Study region

raster September 22, 2105

Variable wind (mean speed ~

3m/s) from ENE to ESE

Western 1/2 raster

September 25, 2015 Wind from NEN, ~5 m/s

October 5, 2015 Wind from NW-N, ~2.9m/s

October 7, 2015 Wind from NE-SE ~ 2m/s

Eastern 1/2 raster October 6, 2015 Wind from N-NW, ~ 3m/s

October 14, 2015 Wind from W-NW, ~5 m/s

Other raster September 24, 2015 Very variable wind (mean ~

2.7m/s) from ENE to ESE

Little Rock and nearby

landfills October 13,2015 NW winds, ~5.6 m/s

Other: facility-level

mostly

September 30, 2015

October 3, 2015

Mostly cloudy flight shortened

Incl. ethane release test

Figure 2-15 shows the location of the 105 discrete air samples collected on ten different

flight days by the airplane over the study region between September 24 and October 7.

The samples were analyzed at the NOAA Boulder labs for the chemicals listed in Table 2-

3 further below.

Figure 2-15: Map showing the location of all flask samples collected by aircraft.


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Links to the final public data sets are available at [11].

2.4.1.2 NOAA Mobile Laboratory

The NOAA Mobile Laboratory (ML, overview in Figure 2-16 and instrumentation in Figure 2-17) surveyed the study region from public access roads to document spatial and temporal gradients in ambient methane and ethane levels.

Figure 2-16: NOAA Mobile Laboratory with air inlets protruding in the front (left). The visual data displays in the front and the instrumentation in the back of the van (right). Location and in-situ Picarro and Aerodyne trace gas measurements are streamed in real time to a display monitor placed in front of the vehicle. Deep cycle batteries in the van allow sampling when the van engine is turned off.

Figure 2-17: Photographs of project-funded instrumentation. Photographs show the ethane, methane, water vapor Aerodyne analyzer purchased for the project. The left photo shows the main instrument in the middle of the rack, with the pump below and three calibration tanks above. A monitor in front of the van displays either the Picarro or the Aerodyne real time measurements for the co-pilot to monitor ambient levels of measured species in close to real-time. The instrument and GPS data are archived in data files and time stamps are used to merge time series.


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The NOAA GMD Mobile Laboratory is equipped with the following equipment:

Cavity ring-down wavelength monitoring methane, carbon dioxide, carbon monoxide and water vapor analyzer (Picarro G2401-m). This instrument can only be operated in one mode with updated measurements sequentially every 2-4 sec (each) for the three trace gases and every ~10 sec for water vapor.

Quantum cascade laser methane, ethane and water vapor analyzer (Aerodyne) with 1Hz measurements.

UV ozone sensor (2B technology)

Discrete air sampling with NOAA aircraft programmable flasks package (PFPs)

system and analysis at NOAA for over 50 chemical species including C1-C6

straight chain alkanes, and several combustion markers such as carbon

monoxide, acetylene and carbon dioxide.

GPS location and time

Instantaneous horizontal wind speed and direction at location of the van

Table 2-2: Summary of 11 drives conducted by NOAA/CU during Fayetteville field study between September 23 and October 8, 2015.

Drive Pattern/Target Dates Notes

Survey September 23, 2015 Began as survey drive and then focused on measurements downwind of poultry farms

Survey September 24, 2015

Survey September 25, 2015 Eastern portion of study area

Facility Focused September 26, 2015 2 gathering stations

Survey September 28, 2015 Incl. landfill outside of study area at end of drive

Facility Focused September 30, 2015 Incl. poultry farms

Survey October 1, 2015 Well pad with separator stuck dump valve, several gathering facilities at end of drive

Facility Focused October 2, 2015 2 gathering stations and one dehydrator station

Facility Focused October 3, 2015 2 gathering stations

Survey October 5, 2015

Survey October 8, 2015 No ethane measurements (broken pump for ARI instrument)


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Figure 2-18 show the tracks of the mobile lab for the 11 drive days, and the locations

where the 156 discrete air samples were collected between September 22 and October

8. As with aircraft samples, these samples were analyzed at the NOAA Boulder labs for

the chemicals listed in Table 2-3 further below.

Figure 2-18: Map showing tracks of the NOAA van for the 11 ground sampling days. Also displayed are the locations of all the flask samples collected by the van (purple squares).

2.4.1.3 NOAA Flasks Analysis

The discrete air samples collected by the airplane and the NOAA van were analyzed at the NOAA GMD laboratory by different chemical analysis systems for CO2, CH4, N2O, CO, H2 and SF6 and also short chain alkanes. A complete list of the GC-MS targeted analytes is given in Table 2-3.

Figure 2-19: NOAA discrete air sampling system package. The package contains 12 glass flasks. Packages like this one are hooked up to a compressor package attached to an inlet line in the NOAA van or the Scientific Aviation aircraft. Once sampled, a package is sent back to the NOAA Boulder labs for analyses, then prepped before getting in the field again. Gaby Petron and research assistant Eryka Thorley were conducting ground sampling operations for the NOAA funded 4 corners study. Photo taken in New Mexico by journalist Tim Gaynor (April 2015).


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Figure 2-20: NOAA Global Monitoring Division MAGICC Greenhouse gas analysis system. Each black package in the front contains 12 air samples connected by a manifold hooked up to the analytical system. Silver gas cylinders in the back are calibration tanks and target tanks. This system and its copy are used 24/7 by our group to analyze close to 20,000 air samples/yr from the NOAA global and North American networks. The extreme sensitivity and reliability of the air sampling and analysis at the NOAA labs make the NOAA flasks data a World Meteorological Organization Global Atmospheric Watch reference used to track the composition of the atmosphere worldwide. The NOAA flasks have also been used to evaluate in-situ field measurements of GHG and associated species during multiple projects funded by NASA, NSF, DOE and others.

d

Figure 2-21: Partially automated NOAA Global Monitoring Division Perseus Gas Chromatography-Mass Spectrometer. (Developed by Dr. Benjamin Miller) Flask packages from the field on the bottom right are hooked up to the system and analyzed for the ~ 60 species listed in the Table below. The analysis time for each flask is about 30 minutes. A target tank of Niwot Ridge,CO whole air –collected by our lab- is injected in the system every 4 flasks to track the instrument response throughout the day.. During the ground samples analysis, the target tank was run after every non-background air sample to track any changes in the instrument response after very polluted samples.


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Table 2-3 Species analyzed on NOAA Perseus GC-MS instrument. Yellow color denotes analytes recently added to NOAA GMD GC-MS target list.

2.4.1.4 NOAA Wind Profiler

NOAA GMD partnered with NOAA Physical Sciences Division to deploy 915-MHz Boundary-layer Wind Profiler (BLWP) in the study region, near the locality of Bee Branch (35.43N; 92.38W), on the western half of the study area. (Figure 2-22) The system operated 24/7 during the entire field study. The BLWP is a pulsed-Doppler radar. 5-min average wind speed and direction were derived from the radar measurements hourly as well as the height of the convective boundary layer. The instrument vertical resolution is 60- or 100-m from near the surface (0.18 km) to as high as 4-5 km above ground level). An example of the resulting measurements is shown in Figure 2-23. More information is available at the NOAA Earth System Research Laboratory (ESRL) Wind Profilers page [16].

Here are some more details to be considered when choosing a site for a NOAA wind profiler deployment:

Preferably, NOAA deploys their profiler systems at sites owned by a government entity and provided to NOAA at no cost. However agreements have also been reached in the past with other types of land ownership.

The box-looking structures occupy an area of 25' x 25'. The equipment trailer is 6' x 12' and requires either 120 V or 240 V power. At 120 V we would like to have 25 amps. A fenced compound is preferable.


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We need to look in two directions with the profiler, 90 degrees apart, without looking at power lines or busy roads.

We use a cell phone for data transmission so decent cell coverage is needed.

Figure 2-22: NOAA ESRL Physical Sciences Division 915 MHz wind profiler as deployed in the field. The site was selected to have a large field of view. The instrument, including computer and communication system (photo to the left), is powered by regular outlet. The NOAA Mobile Lab (retrofitted 11 passenger van) and nearby building give a sense of scale.

Figure 2-23: Hourly vertical profile of 5-minute average horizontal wind speed and direction. Measurements were taken with the NOAA PSD 915 Hz radar deployed near Bee Branch, AR on October 1-3, 2015. The wind barb indicates where the wind is coming from at a particular height and the barbs and color are indicative of the wind speed in knots (10 knots=5meters/sec).

Archived profiler data products are available at ftp://ftp1.esrl.noaa.gov/psd2/data/realtime//Radar915/Images/bbh/2015/

In order to estimate emissions using ambient mixing ratio measurements in the PBL, it is important to track weather patterns, including lower atmosphere transport

ftp://ftp1.esrl.noaa.gov/psd2/data/realtime/Radar915/Images/bbh/2015/


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characteristics (wind speed, direction, cloud cover) and boundary layer dynamics (vertical mixing, PBL height, morning growth and evening collapse).

In-situ, rapid, high-resolution and 24/7 observed vertical profile wind data are necessary to:

o Guide flight planning before each flight take-off and real-time decision-making based on actual observed wind conditions.

o Support the interpretation of the transport history of air masses in the region sampled by the aircraft

o Provide observation- based evidence for selection criteria for mass-balance method applicability

o Conduct accurate mass-balance emission estimation for regional emissions

2.4.2 Facility-scale measurement methods

Protocols for facility-scale measurements are provided in the annexes:

Annex 2: Production Module: Measurement Protocol

Annex 3: Gathering Station Module: Measurement Protocol

The common protocol for utilizing optical gas imaging for source detection and various methods for emission rate measurement are documented in Annex 4: Onsite Detection and Measurement Protocol.

Downwind methods have been documented in numerous recent publications, and are not repeated here. An overview of the Other Test Method 33A (OTM-33A) is included in Annex 2, as it was utilized exclusively at well pads. An overview of the tracer flux method is included in Annex 3. Tracer flux was utilized at both compressor stations (gathering and transmission), and at well pads. Mobile equipment utilized for the study are shown in Figure 2-24.


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Figure 2-24: Instrumented vans deployed for the study. From left to right: University of Wyoming van, 2 Aerodyne vans, and NOAA van. Photograph taken at the central staging area during the campaign.

2.4.3 Methods for gathering pipelines and distribution systems

Measurement methods utilized for gathering pipelines are detailed in Annex 10 (see Figure 2-12 for the measured locations). Measurement teams drove the selected pipeline segments with a vehicle-mounted gas detection system to identify leaks in underground piping. When detected, emissions were isolated and measured utilizing a flux chamber approach. Emissions from auxiliary equipment were detected utilizing optical gas imaging or laser detectors and measured utilizing high-flow instruments.

Similar methods were utilized for distribution system measurements. Annex 11 provides an overview of the methods and instrumentation.

2.5 Summary of field campaign

2.5.1 Well pad measurements

During the field campaign, 261 well pad facilities were measured. 51 of these paired measurements were measured using onsite methods only, as onsite methods proved to be much faster than any of the downwind techniques. Figure 2-25 shows the location of these measurements. With very few exceptions, all measurements were completed within the clusters selected during the study design. (Vehicle surveys, conducted by downwind teams as they moved between locations, were not included in the total measurement count, and often fall outside cluster boundaries).


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Figure 2-25: Map of production measurements. Blue markers indicate sites measured utilizing an onsite survey, yellow markers the OTM33A downwind method, and red markers the tracer flux method. Sites marked in green indicate well pads that were screened during the early part of the campaign (see Section 2.3.3) or were observed from public roads by downwind (tracer or OTM33A) teams while moving between facilities. Screened sites are not included in the total measured well pad count.

Table 2-4 indicates the number of paired measurements, and the number of those pairs where one of the measurements was zero based upon a lower detection limit methodology of one of the downwind methods.

Table 2-4: Contemporaneous measurements at natural gas production facilities

Method 1 Method 2 Paired

Measurements

Zero Measurements

in Pairs Zero Determination Method

Onsite Tracer 16 3 Tracer Limit of Detection

Onsite OTM33A 51 10 OTM33A Transect

Tracer OTM33A 11 3 Tracer Limit of Detection

Wells per well pad was utilized as a surrogate for well pad complexity and size to assess the representativeness of the measured population relative to the study population – i.e. well pads operated by the study partners. Well count is indicative of the size (physical extent) of the facility and the amount of equipment on the facility. Typically


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well pads in the study area generally have 1 wellhead, 1 separator and 1 meter run8 per well, but share produced water tanks. Figure 2-26 shows the fraction of facilities in the study area population and in the sampled population by active well count. Facilities with zero active wells are not included in the data. A normally active well may be classified as inactive if temporarily abandoned, plugged and abandoned, or shut-in for maintenance reasons. Five additional facilities with zero active wells were measured during this study.

Well pads accessible for measurement tended to be larger (mean of 2.3 wells per pad) compared to those not accessible for measurement (mean of 1.6 wells per pad). The measured population is somewhat weighted to larger well pads, including a higher (8% versus 3%) number of well pads with more than 6 wells.

Figure 2-26: Comparison of study population and measured well pad size. Accessible well pads tend to have more wells than well pads that were not accessible to the study team for measurement. Relative to the accessible population, somewhat more measurements were made larger facilities than the accessible population, but average well pad size approximates that of the accessible population.

As noted earlier, an independent onsite observer monitored measurements at the production facilities. The observer noted whenever measurement conditions prevented an incomplete capture of the emissions during measurement; this was only observed to have happened on three attempts to measure tank emissions. Post-campaign, reported measurements below the lower detection limit of the instrument were also identified.

Total measurement counts are provided in Table 2-5. Measurement counts are identified by the location on the well pad where they were made. Each location has

8 A meter run is a section of straight piping equipped with a gas flow meter to measure the gas leaving the well pad. In the study area, well pads were typically equipped with an individual meter for each well. An example can be seen in Figure 2-7.


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several component types, which were screened for emissions and measured (see data tables).

The vast majority of measurement exceptions (203 of 206 total exceptions) are locations where emissions were noted by OGI or laser detector, but the measured emission was at or below the lower detection limit of the measurement instrument. These small emissions sources are as likely to be over-estimated as under-estimated, and were modeled statistically to compute facility-level emissions estimates. Incomplete capture was noted during three of 71 attempts to measure emissions from tank vents, and are also modeled utilizing statistical methods to capture the likelihood of under-estimating emissions. Details of the statistical modeling are provided in Annex 5, which discusses the development of the study onsite estimate (SOE) for production facilities.

Table 2-5: Production Facility Measurement Summary

Location on Well Pad

Number of Measurements

Fraction with

Exceptions

Below Lower Detection

Limit Incomplete

Capture No Exception Total

Compressor 19 37 56 34%

Piping or Gas Line 3 5 8 38%

Separator 81 40 121 67%

Tank Vent 51 3 17 71 76%

Well Head 44 16 60 73%

Other 3 1 4 75%

Location not Noted 2 2 100%

Total 203 3 116 322 64%

Figure 2-27 summarizes the 261 well pads where onsite measurements were completed. Note that these emission source counts include only emission sources detected with optical gas imaging or laser detectors, and do not include planned emissions from gas-powered pneumatics or pumps, or methane in compressor engine exhaust. Approximately half had no detected emission sources (126 facilities), while another third had one or two sources detected. The remaining sixth had three or more sources, but only 2% (5 facilities) had more than five sources.


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Figure 2-27: Production emission source count by facility

Figure 2-28: Correlation between count of sources and measured emissions on production facilities

There is essentially no correlation between the number of sources measured and the emissions measured at the facilities, as shown in Figure 2-28 above. The largest six measurements, five of which were tank vents, range from 3.3 to 8.8 kg/h and account for 50% of measured emissions.


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As has been noted in most recent onsite measurement campaigns [12]–[15], [17], some sources measured here exhibit a “long tail” where a small number of measured sources (locations) constitute a disproportionate share of total emissions. Two examples are provided in Figure 2-29 for tank vents (49 total measurements) and in Figure 2-30 for liquid level controllers (39 total measurements). Incomplete captures are not included in these plots. Cumulative distribution functions are shown in panel (a) on both figures, while cumulative emissions are shown in panel (b). For tanks, the highest-emitting 5% of sources account for ≈63% of emissions. Liquid level controllers exhibit less, but still substantial, skew – the largest 15% of sources account for ≈66% of emissions; the largest 5% of sources account for ≈27% of emissions.

Figure 2-29: Tank vent emissions measured at production facilities. The highest-emitting 5% of measured sources accounted for ≈63% of measured emissions. Three locations exceeded 3 kg/h. Incomplete captures are not included in this figure.


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Figure 2-30: Liquid level controllers measured at production facilities. The highest emitting 5% of measured sources accounted for 27% of measured emissions. The highest emitting 15% accounted for two-thirds of measured emissions.

Downwind measurements were also completed at well pads. The 71 successful well pad measurements made using OTM 33A spanned five orders of magnitude, as shown in Figure 2-31. As described in the measurement protocols in Annex 2, OTM measurements are reported as zero emissions if the OTM team did not detect methane concentrations elevated above background values while driving around equipment onsite, very near to potential emission sources. Thirteen measurements were indicated as zero using this method. Remaining measurements were made utilizing standard OTM 33A methods. The largest measurement, 63 kg/h, was made at well pad ID 371, where a manual unloading was occurring, and accounted for 39% of total emissions measured.

Figure 2-31: Distribution of OTM 33A measurements

Measurement distributions are summarized in Figure 2-32. As with onsite measurements the data exhibits a skewed distribution. For the facilities measured, the


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highest-emitting 5% of facilities measured accounted for 68% of total emissions measured, and the largest 10% of facilities accounted for 80% of total measured emissions.

Figure 2-32: OTM 33A measurements made at well pads. Mean values shown for all measurements. Measurements are classified by the measurement method. “Zero by Transect” indicates that the measurement team did not detect methane emissions above background levels while driving around and near equipment on the well pad. “Measured” indicates that a downwind measurement, using the OTM 33A protocol, was completed.

Tracer flux measurements for the 17 well pads measured in the study are summarized in

Figure 2-33: Measurements made at well pads using tracer flux method. Mean values shown for all measurements. “Limit of Detection” indicates measurements were no methane enhancements were detected downwind of the facility, and an estimated, site-specific, lower limit of detection is utilized for comparisons. “Measured” indicates a successful measurement made utilizing the tracer flux protocols. A manual unloading at one well pad dominates total emissions with a measured emission rate of 810 kg/h, as indicated by the split X axis.


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. Tracer measurements captured one manual unloading of one well at well pad ID 371. The emissions measured at this site (810 kg/h) accounted for 97.5% of all emissions measured by tracer methods during the field campaign.

Figure 2-33: Measurements made at well pads using tracer flux method. Mean values shown for all measurements. “Limit of Detection” indicates measurements were no methane enhancements were detected downwind of the facility, and an estimated, site-specific, lower limit of detection is utilized for comparisons. “Measured” indicates a successful measurement made utilizing the tracer flux protocols. A manual unloading at one well pad dominates total emissions with a measured emission rate of 810 kg/h, as indicated by the split X axis.

Excepting the manual unloading measurement, distribution of tracer measurements do not exhibit the strong measurement skew seen in most other emissions distributions seen in this study (Figure 2-34). The top 5% of measurements account for 32% of total measured emissions. Measurements are also compressed in dynamic range compared to those of OTM 33A or facility-level estimates using on-site emissions. Tracer measurements span three orders of magnitude (not counting those at/below the lower limit of detection).


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Figure 2-34: Cumulative measured emissions for tracer measurements at well pads, excluding one manual unloading. Classification labels follow those summarized in Figure 2-33. Emissions span three orders of magnitude, and do not display significant skew, unlike other facility-level emission distributions for well pads from this study.

2.5.2 Gathering station measurements

Gathering stations were measured as indicated previously. Study team supervisors dispatched measurement teams with an emphasis on paired measurements. As a result, the facilities available for measurement were restricted to those with road access on the south side of the facility, at a suitable distance for tracer flux methods, due to the northerly winds throughout the field campaign. When all suitable facilities within the study clusters were measured, measurements were extended to facilities outside the cluster area. Facilities at which measurements were attempted are illustrated in Figure 2-35. Figure 2-36 illustrates two gathering facilities, one that that could not be measured due to the road access restraints, and another that could be measured.


44

Figure 2-35: Measured gathering and boosting facilities. Study clusters are indicted in red and blue and the approximate outline of the study area in yellow. Transmission pipelines are included for reference. Gathering facilities were measured outside clusters when all suitable compressor stations within the clusters had all been measured.


45

Figure 2-36: Example of typical gathering station road access. Top facility (red circle) could not be measured due to: (1) the access roads to the south are too close to the facility for tracer measurements, and (2) public roads are in the wrong direction for the wind. Bottom facility (green circle) has good road placement for northerly winds, particularly on public roads to the south.

Study partners operate 95 gathering compressor stations in the study area, and measurements were performed at more than one third of these stations. This high

Access road

Public Road

Road Access

Gathering StationG

North

North


46

fraction for measurement indicates that the field campaign attempted measurements at nearly all facilities measurable by tracer given the wind conditions. Since the direction of the wind was not known prior to the study, wind direction served as the predominant randomizer to select facilities for measurement.

Not all measurement attempts resulted in successful measurements. For aircraft or tracer methods, measurements may be excluded due to an inability to isolate emissions originating at the facility from other nearby emission sources, the wind direction or strength may be insufficient for plume transport, or changes in operating conditions at the facility may produce too much variability to produce reliable measurements downwind. Onsite measurements may fail if the measurement is incomplete (e.g. team ran out of time), equipment failure, or inaccessibility of too many emission locations. Table 2-6 summarizes the results of the measurement campaign. While final determination of method success/failure was made during post-campaign quality control, most often the measurement teams identified issues during field work and noted these issues in field notes for later analysis.

Table 2-6. Gathering and boosting station measurements. Thirty-six gathering and boosting stations were visited by 3 independent measurement teams, collectively: Onsite, Tracer, and Aircraft. “Failed” indicates that the facility was not successfully quantified by a given measurement method, and was removed from further analysis.

Method # Attempted # Failed # Successful

Onsite 33 1 32

Tracer 32 2 30

Aircraft 11 1 10

The representativeness of the measurements was analyzed by utilizing total compressor power as a surrogate for the size and complexity of gathering stations. The distribution of measured stations was compared to the study population – i.e. gathering stations operated by the study partners. Figure 2-37 illustrates that degree of agreement between measured and total population, and a two-sample Kolmogorov-Smirnov test indicates statistical agreement for a confidence interval of 95%.


47

Figure 2-37: Test of representativeness for gathering stations. The cumulative distributions of total facility compressor power indicate that the measured facilities are representative of the available population.

At gathering stations, teams inspected all possible emission points at the facility using optical gas imaging. A total of 484 emission sources were detected and measured utilizing high-flow instruments. An additional 26 sources were detected with the camera but could not be measured (due to safety or accessibility reasons). For these sources, site observers noted the equipment and component type as “observed / not measured.” To estimate total emissions for a facility, these sources were statistically modeled utilizing other measurements on similar components. Post campaign, measurements were screened to identify measurements above the instrument’s measurement range or below the lower detection limit of the instrument. These were also flagged, and are noted in Table 2-7. Statistical methods were also utilized to estimate emissions for both categories, and are described in Annex 6.

Table 2-7: Onsite device measurements at gathering compressor stations

Measurements below the lower detection limit (170 total) represent small emission sources that were seen by the camera, but were too small to capture and/or measure

Over

Instrument

Range

Below Lower

Detection

Limit

No

Exception Total

Number of

Sources

Fraction of

Total

Sources

Compressor 5 80 208 293 29% 16 5%

Dehydrator 20 15 35 57% 0%

Pig Launcher or Receiver 1 1 0% 0%

Piping or Gas Line 15 25 40 38% 0%

Separator 27 25 52 52% 0%

Tank 2 9 11 18% 8 42%

Other 26 26 52 50% 2 4%

Total 5 170 309 484 36% 26 5%

Observed Not MeasuredNumber of Measurements

Location at Compressor

Station

(Equipment Type)

Fraction of

Measurements

with

Exceptions


48

using the instrument. In addition, since these cameras work in the IR spectrum, camera operators occasionally mistake heat emissions for methane emissions near hot components of compressors or dehydrators.

Table 2-8 summarizes the components where camera operators observed emissions, but measurements could not be made. These sources span a wide range of source locations, ranging from piping connectors to rod packing vents. Some locations were routed to inaccessible locations (e.g. rod packing into engine fan intake), or could not be reached due to facility configuration or safety concerns (e.g. high roofline vents or tanks without safe access catwalks).

Table 2-8: Location of observed / not measured sources at gathering compressor stations

Location (Equipment

Type) Component Type

Number of Observed /

Not Measured Sources

Compressor

Connector 4

Rod Packing Vent 4

Other 3

Regulator 2

Pneumatic Controller 1

Tank Vent 1

Union 1

Tank Tank 6

Other 2

Other Regulator 2

The source of emissions is further classified in Table 2-9. For this analysis, emission sources were simplified from the detailed data provided in the attached data tables. Nearly two thirds (61%) of measurements were completed on compressor-related equipment. As with a recent study in transmission and storage (T&S) [14], measurements on rod packing vents were one of the largest counts (89). For operating compressors – nearly all compressors surveyed in the study – rod packing vents are designed to have some emissions through the seal area on the cylinder rod.

Unlike T&S compressors, however, a substantial number of sources were identified on liquid separators attached to the compressors. These liquid drop-out tanks are located on inter-stage piping to drop out liquids which were not removed before the compressor by well pad and gathering facility separators. Multi-stage compression is used less frequently in transmission, and/or inter-stage drop out tanks are located outside the compressor building as part of the yard piping from the compressor.


49

Therefore, this source represents a compressor emission source that is somewhat unique to gathering systems.

Figure 2-38a highlights other contributors to compressor measurements. Emissions were found in a small number of over-pressure relief valves (13 of 293 compressor measurements).

Yard piping includes suction and discharge lines, pig launchers and receivers, engine fuel systems, blocking valves for piping routed through the facility, and other miscellaneous equipment. Measured sources are illustrated in Figure 2-38b. Approximately half of all measurements were made on gas pneumatic devices of all types. The remaining measurements were spread over a wide range of equipment components, ranging from general components like unions, flanges and connectors to “open-ended lines” (e.g. station blowdown vents). About half of these measurements (44%) fell at or below the lower detection limit of the instrument.

Table 2-9: Source of onsite measurement by equipment and component types

Figure 2-38: Distribution of measurement locations for two key source categories at gathering

stations

Liquid Level

Controller

Piping

Component

Other

Pneumatic

Device

Rod Packing

Vent

Pressure

Reducing

Valve Tank Vent Other

Compressor 90 57 18 89 13 26 293

Yard Piping 33 48 12 93

Separator 24 8 11 9 52

Dehydrator 13 11 11 35

Tank 4 5 2 11

Total 114 111 92 89 13 5 60 484

Component Type

Location

(Equipment

Type) Total

Liquid Level Controller

31% Rod Packing Vent30%

Piping Component

20%

Other9%Pneumatic

Device6%

Pressure Reducing

Valve4%

(a) Compressor

Pneumatic Device52%

Piping Component

35%Other13%

(b) Yard Piping


50

Separators (not including drop-out tanks attached directly to inter-stage piping on compressors) and dehydrators also had a large proportion of small measurements at or below the instrument detection limits, as shown in Figure 2-39. Tanks and compressors had fewer of these small measurements.

Figure 2-39: Gathering station measurements classified by exception type

Compressors account for 81% of the total emissions estimated at gathering stations. As indicated in Table 2-10, rod packing vents accounted for more than a third of these emissions (41%), followed by piping components and liquid level controllers, which each accounted for 17-18% of compressor emissions. The next largest equipment category, dehydrators, accounted for 8% of measured emissions. These emissions do not include estimated still column vent emissions, which could only be measured in rare cases. These top two categories – compressors and dehydrators – represent the most complex equipment installations on the facility.

Overall, the 89 rod packing measurements account for a third of emissions measured onsite. The next four largest categories each accounted for a similar fraction of total emissions (12-16%). A second compressor-specific component, pressure reducing valves, accounted for 12% of total emissions. Together, these two compressor-only categories account for nearly half of all emissions.


51

Table 2-10: Summary of emissions measured onsite at gathering stations. Measurements include values reported below/above the operational range of the instrument, but do not include measurements from downwind teams that specifically targeted a portion of the facility’s emissions, such as a tank battery.

Figure 2-40: Total measured emission by component category

Pneumatic devices – split here into liquid level controllers and other pneumatic devices – accounted for a quarter of measured emissions. The majority of the liquid level controller measurements (90 of 114) were associated with compressors, and emissions from all measured liquid level controllers are statistically larger than those measured on other pneumatic devices. Figure 2-41 shows cumulative distribution functions (CDFs) for both measurement sets. Emissions are statistically different using a two-sided Kolmogorov-Smirnov (“KS”) comparison at the 95% confidence level. It is therefore interesting to look at these two pneumatic device types separately, and this finding may indicate a need to maintain this distinction in future measurement campaigns.

Rod Packing

Vent

Piping

Component

Liquid Level

Controller Other

Pressure

Reducing

Valve

Other

Pneumatic

Device Tank Vent

Total

(kg/h)

Fraction

of Total

Compressor 101.8 42.0 44.4 14.5 36.0 8.7 247.3 81%

Dehydrator 1.5 20.6 0.9 23.0 8%

Yard Piping 6.0 5.4 5.1 16.5 5%

Separator 0.7 4.7 2.8 0.4 8.6 3%

Tank 0.6 3.1 4.7 8.3 3%

101.8 50.1 49.1 43.9 36.0 18.3 4.7 303.7

34% 16% 16% 14% 12% 6% 2%

Location

(Equipment Type)

Total Measured Emission Rate by Component Type (kg/h)

Total Emissions

Total

34%

16% 16% 14%12%

6%2%

0.0

20.0

40.0

60.0

80.0

100.0

120.0

Rod PackingVent

PipingComponent

Liquid LevelController

Other PressureReducing

Valve

OtherPneumatic

Device

Tank Vent

Tota

l Mea

sure

dEm

issi

on

s (k

g/h

)

Component Category

Labels: Fraction of total measured emissions


52

Figure 2-41: Measured pneumatic device emissions from gathering stations

Emission measurements for gathering stations display skewed distributions, similar to those identified in production measurements. Complete data is available in the study data sets. However, it is informative to consider two of the source categories where there are 90+ measurements, the categories account for a significant fraction of measured emissions, and measurements originate from a small number of equipment categories.

Emission rate distributions are shown for measurements of rod packing vents in Figure 2-42. For these source locations, 5% of measurements account for 35% of emissions measured, and 10% of measurements for 55% of total emissions measured.

Figure 2-42: Emission rate distributions for rod packing vents at gathering compressor stations

0.0

0.2

0.4

0.6

0.8

1.0

0.0 1.0 2.0 3.0 4.0

Frac

tio

n o

f M

easu

rem

en

ts

Emission Rate (kg/h)

Liquid Level Controllers Other Pneumatic Devices


53

Emission distributions show a similar “long tail” for liquid level controllers, shown in Figure 2-43. For this emission source category, 5% of measurements account for 32% of emissions measured, and 10% of measurements for 53% of emissions measured.

Figure 2-43: Emission rate distributions for liquid level controllers at gathering compressor

stations

This type of skewed distribution is similar to other recent studies, and indicates the need to characterize emissions distributions at the device level, in addition to “long tail” emissions at the facility level.

All facilities measured in the study had at least two onsite device measurements, and two thirds of the facilities had 6-20 measurements (Figure 2-44). Four facilities had 31 or more measurements, and the highest emitting facility also had the most measurements, 49. Despite this single point, however, there is essentially no correlation between the number of emission sources detected and measured at a facility and the total emission rate measured (see Figure 2-45). While there is some apparent trend in the data, the 𝑅2measured indicates that virtually none of the variation in total emission rate can be explained by the number of measurements performed.

Facilities with the smallest number of total measurements (5 sites with 2-5 measured sources) also had the largest number of observed / not measured sources, averaging two per facility (range of 1-5). All other facilities had 0-2 observed / not measured sources. No one source type stands out as the primary cause of these unmeasured sources.


54

Figure 2-44: Number of measurements and observed, not measured, sources at gathering

compressor stations

Figure 2-45: Emission rate measured related to number of measurements for gathering stations

Based upon total emission rate from onsite device measurements alone – i.e. not considering combustion exhaust methane, unmeasured sources, or other modeled emission sources – the emission rate distribution shows little of the “long tail” see in individual device categories, with the exception of the largest emitting facility, which is responsible for a quarter of all measured emissions, as shown in Figure 2-46.

0.0

1.0

2.0

3.0

4.0

5.0

0

2

4

6

8

10

1-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50

Nu

mb

er o

f O

bse

rved

/ N

ot

Mea

sure

d

Sou

rces

Nu

mb

er o

f Fa

cilit

ies

Number of Measurements Made on Facility

Facility Count Average Number of Observed / Not Measured Sources

67%


55

Figure 2-46: Distribution of total emission rate from onsite device measurements at gathering

compressor stations

Downwind tracer measurements were made at 30 gathering stations. As shown in Figure 2-47, only one measurement exceeded 200 kg/h. This particular site was undergoing maintenance, and the major emission source was identified as an intentionally opened valve.

Figure 2-47: Distribution of facility-level emission rates from gathering stations, as measured by dual tracer release. Including one facility that was undergoing maintenance, where a valve was intentionally left open by operators.

Figure 2-48 shows the distribution of gathering stations with the facility undergoing maintenance removed. Excluding this facility, the 3 highest emitting gathering stations contribute 22% to the total emissions measured by tracer. Continuous tank venting was noted at one of these facilities, however the root cause could not be identified. This facility and the one undergoing maintenance were the only two measured gathering stations where throughput normalized emissions exceeded 1%.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 20.0 40.0 60.0 80.0 100.0

Frac

tio

n o

f Fa

cilit

ies

(-)

Total Emission at Facility (kg/h)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.5 1.0

Frac

tio

n o

f Em

issi

on

s

Fraction of Facilities


56

Figure 2-48: Distribution of facility-level emission rates from gathering stations, as measured by dual tracer release, not including one facility that was undergoing maintenance.

Additional analysis and comparison between methods is available from Vaughn, et. al. [18]

2.5.3 Gathering pipeline measurements

Approximately 96 km of gathering pipeline were screened, including screening and measurement at 95 auxiliary equipment locations.

Emissions were measured at 56 pigging facilities. Emission distributions are summarized in Figure 2-49 and emission locations are detailed in Figure 2-50. Total measured emissions from these facilities were less than 1 kg/h. As with other measurements in this study, emissions at pigging facilities exhibit a skewed emissions distribution. No emissions were measured at 25% of facilities (44 locations), and the lowest emitting 78% of facilities (44 locations) had emissions of less than 10 g/h and accounted for 11% of total measured emissions. In contrast, the highest emitting 5% of facilities accounted for half (47%) of total measured emissions.


57

Figure 2-49: Methane measurements at 56 pigging facilities.

Study teams identified emission locations using laser gas detectors to localize the emission location, which were then measured using a high flow instrument. In many cases, emissions were at or below the detection limit of the high flow instrument – i.e. detected emissions were too small to measure. Emission locations for pigging facilities are summarized in Figure 2-50. With the exception of door leaks, more than 80% of measurements in each category were ≤ the lower detection limit of the instrument.


58

Figure 2-50: Emission locations at pigging facilities. Emission factors identified here may not be broadly representative, due to the restricted range of facility age, type and diversity of operator.

Emissions were measured at 39 blocking valves. Emission distributions are summarized in Figure 2-51 and emission locations are detailed in Figure 2-52. At over half (56%) of block valve locations, no emissions were detected or measured, and 85% of facilities had emissions at or below the lower detection limit of the measurement instrument. Block valve emissions are as skewed as pigging facilities, with 5% of facilities accounting for 47% of measured emissions.

Figure 2-51: Methane measurements at 39 block valve locations.

Location

Total Measured

Emission (g/h)

Number of

Measurements

(count)

Mean Emission

Rate (g/h)

Emissions

Rate 95% CI Range

Number of

Measurements

≤ LDL

Doors 391 13 30 -69% / +95% LDL to 171 g/h 86%

Flanges 22 9 2 -4% / +9% LDL to 3.3 g/h 98%

Gauges 2 1 LDL -LDL+0% NA 100%

Valve Packing 327 50 7 -60% / +85% LDL to 106 g/h 82%

Complete

Facility 742 56 13 -52% / +66% LDL to 171 g/h 50%

(b) Pigging Facilities Emission Locations

LDL = Lower detection limit of the measurement equipment


59

Figure 2-52: Emission locations at block valve locations. Emission factors identified here may not be broadly representative, due to the restricted range of facility age, type and diversity of operator.

Combined together, all measurements at auxiliary equipment facilities on gathering pipelines accounted for less than 1 kg/h (827 g/h). In contrast, the single underground pipeline leak discovered during the campaign had a measurement emission rate of 4 kg/h. The leak was discovered by the vehicle-mounted measurement system, and atmospheric methane concentrations near the leak exceeded 10 ppm 37 meters from the leak and exceeded 11,000 ppm near the leak source, a hole in the ground. While the sample size and diversity from this study is too small to draw national conclusions, evidence accumulated here indicates that pipeline leaks, although significantly rarer than auxiliary equipment leaks, have the potential to be the dominant source of emissions for the gathering pipeline network.

2.5.4 Distribution system measurements

Distribution measurements were performed as indicated. Considering the entire field campaign, 100% of above-ground facilities were measured in Faulkner, Van Buren,

Location

Total

Measured

Emission (g/h)

Number of

Measurements

(count)

Mean Emission

Rate (g/h)

Emissions Rate

95% CI Range

Number of

Measurements

≤ LDL

Doors

Flanges 7 3 LDL 0% / +0% NA 100%

Gauges 4 1 NA -LDL+0% NA 100%

Valve Packing 74 21 3.5 -40% / +54% LDL to 18 g/h 90%

Complete Facility 85 39 2.2 -57% / +74% LDL to 21 g/h 85%


(b) Block Valve Emission Locations


60

Conway, and Cleburne County. The field campaign ended while measuring in White county, resulting in 44% of facilities measured. No measurements were made in independence or Jackson County due to time limitations.

Emissions were measured at 100 Regulators, and 29 Transmission/Distribution Transfer Stations where the only the distribution side was measured at 4 and only the Transmission side was measured at 2 stations. Emissions distributions are summarized in Figure 2-53.

Figure 2-53 Methane measurements at distribution network facilities

Emission Identification was performed using the same approach that was used in identifying emissions from auxiliary equipment locations on gathering pipelines. In many cases, emissions were at or below the detection limit of the high flow instrument – i.e. detected emissions were too small to measure. A summary of emissions at facilities in the distribution network is detailed in Figure 2-54.


61

Figure 2-54 Emission facilities in the distribution network. Pneumatic emissions are not necessarily fugitive emissions but are due to normal operating processes.

A total of 34 reported leaks were visited, that were randomly selected from a list of 108 leaks. Of the measured leaks 20 were found on service lines and 14 were found on main lines. Emission distributions are summarized in Figure 2-55.

M&R4%

TDTS Distribtuion Side

2%

TDTS Transmission Side24%

Pneumatic Devices at TDTS

70%

(a) Total Measured Emission

Location

Total Measured

Emission (g/h)

Number of

Measurements

(count)

Mean Emission

Rate (g/h)

Emissions

Rate 95% CI Range

Number of

Measurements

≤ LDL

M&R 99.5 100 1 -79% / +141% LDL to 63 g/h 85%

TDTS Distribtuion

Side 48.2 27 2 -88% / +126% LDL to 24 g/h 78%

TDTS

Transmission 549.8 25 22 -93% / +154% LDL to 358 g/h 68%

Pneumatic

Devices at TDTS 1651.3 8 206 -48% / +49% 47 to 396 g/h 0%

(b) Type of Facilities Emissions



62

Figure 2-55 Methane measurements at 34 reported leaks.

Emissions came from two categories, either mains or services and are described in Figure 2-56

Figure 2-56 Emission pipeline types. Emission factors identified here may not be broadly representative, due to restricted range of facility age, type and diversity of operator

Services61%

Mains39%

(a) Total Measured Emissions

Location

Total

Measured

Emission (g/h)

Number of

Measurements

Mean Emission

Rate (g/h)

Emissions Rate

95% CI Range

Number of

Measurements

≤ LDL

Services 343 20 17.153 -91% / +115% LDL to 155 g/h 20%

Mains 220 14 15.685 -91% / +154% LDL to 164 g/h 21%


(b) Leak Type Emissions


63

Combined together, all measurements from the distribution network accounted for less than 3 kg/h (2911.6 g/h). It should be noted that pneumatic emissions dominate this total accounting for 57% of total emissions putting fugitive emissions at 1.26 kg/h.

2.6 Emission estimation for facilities

The study onsite estimate (SOE) utilizes onsite device-scale measurements (ODMs) for measured emission sources and engineering estimates for unmeasured emissions sources to develop a facility-scale estimate of emissions for well pads and gathering stations. The SOE for well pads and gathering stations are compared with estimates from Tracer, OTM33A, and aircraft spiral flights for measured facilities to evaluate the accuracy of the two facility-level SOE models. Tracer facility estimates (TFEs) for facilities were developed utilizing methods in [1], [13] and are documented in Yacovitch et. al. [19] OTM33A facility estimates (OFEs) were developed utilizing methods from Brantley, et. al. [2] and results are documented in Robertson, et. al. [20] Aircraft facility estimates (AFEs) were developed utilizing methods in Conley, et. al. [in preparation]. Summary data tables from these methods used in facility-scale comparisons are provided in Annex 12: Production Measurements, and Annex 13: Gathering Station Measurements.

SOEs are used in the basin-scale model to derive emission estimates for all well pads and gathering stations in the study area, using detailed activity and equipment counts provided by operators for the time window of the campaign.

2.6.1 Facility estimation and comparisons for well pads

For each well pad in the study area an SOE was developed utilizing Monte Carlo methods. Measured emissions from the field campaign were combined with engineering estimates for all other sources to develop an estimate including all identified or likely sources at the well pad. Annex 5: Production Module: SOE Development provides an overview of the modeling methods utilized to develop a facility-scale estimate for well pads.

While an SOE was developed for all well pads for the study area model, additional analysis was performed on the 58 well pads where paired measurements were performed during the study. Results are shown in Figure 2-57. The mean estimate from each method (bars) is shown with error bars depicting 95% confidence bounds as determined by the method (see annexes). Table 2-11 summarizes the number of paired measurements by methods and the number of observations with overlapping confidence bounds.

Mean tracer estimates are greater than the study onsite estimate at 10 of 16 (63%) paired production pads; however, 15 of 16 (94%) estimates are statistically similar. The facilities with the largest emission rates are dominated by liquid unloading emissions, which can be large, but short-duration emissions from the perspective of typically


64

annualized emissions in inventories. For these facilities, SOE and tracer agree statistically, and means are within a factor of two for the facilities with emissions greater than 1.5 kg/h, which account for 99.4% of emissions for facilities where tracer-SOE comparisons are possible. In this set of facilities, one well pad with a manual unloading accounted for the 93.7% of total measured emissions, and for this well pad the mean tracer estimate was a factor of 2 higher than the SOE. Tracer and SOE agree within uncertainties at 11 out of 12 sites without liquid unloading. Emissions estimated by SOE at these 12 facilities range from 0.03 to 1.9 kg/h. The SOE at these sites total 6.2 kg/h, 60% of the total 10.3 kg/h estimated by tracer.

The relative agreement of tracer with SOE (overlapping uncertainties) indicates that the models utilized for unloadings are representative of the single manual unloading which was measured during the field campaign. Given the small sample size (1 facility), it is impossible to determine if the offset in the mean estimates is indicative of a low bias in the SOE models.

Table 2-11: Statistically similar method comparisons at well pads

Method 1 Method 2 Paired

Measurements

Method 1 >

Method 2

Statistically Similar

Estimates

Tracer Onsite 16 10 (63%) 15 (94%)

Onsite OTM33A 51 44 (86%) 27 + 9 zero based on

transect (71%)

Tracer OTM33A 11 10 (91%) 8 (73%)

Table 2-12: Tracer/SOE comparison for highest-emitting well pads

Facility Ranked by

SOE

Study Onsite Estimate (SOE) Tracer Estimate

Ratio of Mean Tracer to Mean SOE

Fraction of Total Paired

Mean (kg/h)

95% Confidence

Interval (kg/h) Mean (kg/h)

95% Confidence

Interval (kg/h)

1 403.3 -235.7/+550.5 810.4 ±207.2 2.0 93.7%

2 19.7 -16.8/+24.0 8.3 ±3.2 0.4 4.6%

3 1.9 -0.2/+0.3 2.9 ±0.8 1.5 0.4%

4 1.6 -0.1/+0.2 1.6 ±0.4 1.0 0.4%

5 1.0 -0.9/+5.2 1.5 ±1.5 1.6 0.2%

6 0.2 -0.2/+1.6 1.3 ±0.9 5.6 0.1%

7 0.2 -0.1/+0.2 1.3 ±1.3 6.2 0.0%

Group Total 428.0 827.4 1.9

All Paired 430.3 830.9 1.9


65

Figure 2-57: Paired measurements performed at 58 at production pads. Facilities are shown in decending order of the study onsite estimate magnitude. Paired production pads include 3 facilities measured by tracer where only an upper limit is reported using the limit of detection method (production pads 34, 36 and 50), and 10 facilities reported as zero based on transect by the OTM33A measurement team (production pads 25, 32, 43, 48, 49, 51, 53, 54, 55 and 58).


66

OTM33A reported zero based on transect at 10 of 51 sites paired with onsite measurement. At 9 of these sites the SOE consists of only simulated pneumatics (the onsite team observed no emission sources). In addition to 27 other sites with overlapping confidence bounds, these estimates may be considered statistically similar since it is possible that no pneumatic actuations may have occurred during the OTM33A measurement. The OTM33A estimates are lower than the corresponding SOE at 44 of 51 (86%) paired sites, however the estimates may be considered statistically similar at 36 (71%) production pads.

2.6.2 Facility estimation and comparisons for gathering stations

A study onsite estimate (SOE) was developed for each gathering station in the study area utilizing Monte Carlo methods. Onsite measurements were combined with engineering estimates for unmeasured sources to develop a facility-level methane emission rate. Annex 6: Gathering Station Module: SOE Development provides an overview of this procedure. Study onsite estimates were compared to tracer facility estimates (TFE) at 24 gathering stations and aircraft facility estimates (AFE) at 6 gathering stations. Facilities where aircraft measurements were also made are marked with a * in Figure 2-58. Comparisons between techniques were only made at facilities where no episodic events or major changes in operational state occurred during measurements by either method, and no evidence of interfering emissions from neighboring facilities was found.

Figure 2-58: Study onsite estimate and tracer facility estimate comparison at 24 gathering stations. * An aircraft facility estimate was also made at this gathering station.


67

In Figure 2-58 and Figure 2-59 bars indicate mean facility-level emission rate estimates, and error bars represent 95% confidence intervals for each technique. SOE confidence intervals overlap tracer release estimate confidence intervals at 20 of 24 gathering stations, indicating a statistically similar facility-level CH4 emission rate estimate. SOE and tracer facility estimates do not show statistical agreement at 3*, 12, 18, 21. Statistical agreement between SOE and TFE does not appear to be dependent upon emission magnitude.

Figure 2-59: Study onsite estimate and aircraft facility estimate at 6 gathering stations.

AFE and SOE confidence intervals overlap at 4 of 6 paired measurement facilities. AFEs and SOEs do not overlap at Station 7* and 17*, but do at facilities with larger and smaller emission magnitudes. TFE are also shown for these gathering stations.

Cumulative totals for gathering stations where comparisons were made are shown in Table 2-13, with cumulative confidence intervals derived from facility level confidence intervals added in quadrature. SOE and TFE cumulative emissions show statistical agreement with TFEs accounting for 89% of SOEs at 24 compared facilities. The relatively good agreement between these techniques indicates that the engineering estimates used in SOE are robust, and that TFE are capable of quantifying gathering station emission accurately at the facility-level. Cumulative emission rate estimates for


68

the six facilities where all three techniques were compared show statistical agreement as well.

Table 2-13: Cumulative facility-level emission rates at compared gatherings stations.

Cumulative CH4 Emissions at Gathering Stations 24 stations

(kg/h) 6 stations

(kg/h)

Study Onsite Estimate 1349 ± 143 371 -44/+42

Tracer Facility Estimate 1204 ± 401 301 ± 74

Aircraft Facility Estimate 468 ± 119

2.6.3 Emission estimation for other facilities and methane emission sources

No paired measurements were made for gathering pipelines (pipelines or auxiliary facilities), transmission stations, or distribution system (pipelines or above-ground facilities). Transmission facility measurements were made utilizing tracer flux methods and modeled in study area emissions directly. Non-oil & gas emissions, primarily from farm operations in the study area, were modeled also modeled directly in a spatially-resolved model for the study area.

2.7 Emission estimation and attribution for the study area

2.7.1 Aircraft Mass Balance

The regional scale aircraft mass balance method has been applied by NOAA/CU and partner investigators to characterize methane and non-methane hydrocarbons emissions from several US onshore oil and gas producing basins [4-8].

The method’s first known application targeted urban emissions of ozone precursors [White et al., 1976].

The sampling method and data analysis approach have been described extensively in previous publications [4], [6]. Here we go over the method in some details relevant for readers interested in the atmospheric sampling and data analysis implementation and we use graphics and tables to support the description.

The basic principle behind the approach is mass conservation. For a long-lived tracer like methane (10-year global mean turnover lifetime), the change in the trace gas abundance in an air mass transiting over a source region is directly related to total emissions of methane in the region during the transit time. Under ideal meteorological conditions, the difference between the methane flow rate through the study region’s downwind border (outflow) relative to the methane flow rate through the study region’s upwind border (inflow) equals the emission rate accumulated over the entire region (Figure 2-60 below).


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Figure 2-60: Schematic of the mass balance concept. The difference in methane abundance between the outflow and inflow for the study region is directly related to emission of methane

within the study region.

The aircraft Mass Balance method used here is an indirect method to quantify total methane emissions from the study region. One can imagine setting a virtual box chamber over an area source of interest and sampling the air in the inflow and outflow as well as measuring the airflow rate through the chamber. In our case, we cannot control the flow rate or install a virtual box chamber. Instead the air mass movement in the boundary layer directly above the surface where emissions happen is dictated by the wind patterns, presence or lack of convective clouds, and small scale turbulent mixing in the region of interest. Methane has a global mean atmospheric turnover time of 9 to 10 years. Its main sink is oxidation by hydroxyl radicals (OH). This means that at the scales covered by a 5-6 hour flight, we can ignore the chemical destruction of methane.

The analogy with the box chamber experiment makes it clear that specific meteorological conditions are required to apply this simple box model method to derive a regional emission rate estimate. These necessary conditions and the ensuing atmospheric sampling procedure and requirements are listed in Table 2-14 below.

Table 2-14: Regional mass balance implementation requirements.

Atmospheric condition requirements for Mass

Balance Sampling requirements Observations

Steady and uniform wind speed and direction in PBL over entire region

Horizontal wind speed and direction from aircraft (in situ along flight track only) and NOAA wind profiler (24/7 throughout lower


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atmosphere, resolved vertically at one location in the field)

Fully developed boundary layer

Minimal relative changes in PBL height

Flight during afternoon hours. Measure PBL height with ground-based system (here based on data from wind profiler) and/or aircraft.

Well-mixed boundary layer

PBL convective mixing has to be strong enough to mix surface emissions in the vertical in the PBL within 10-20 minutes

Flight during afternoon hours. When possible, conduct multiple downwind transects at different altitudes within PBL. Downwind transect flown 6 to 10 miles downwind of “last” sources in study region ensures PBL mixing ratios are uniform in vertical and sampling at 1 (or more) altitude is representative of entire PBL column.

Clear skies No loss through the PBL top (“cloud venting”)

Flight during cloud free afternoon

The study team had daily weather calls with the Little Rock National Weather Service forecast office early in the morning to finalize the plan for the same day flight and start planning for the next 1 to 3 days.

An example of the email request we send to the local Meteorologist In Charge is below:

NWS local office will provide a short (5-15 minutes or so) briefing at 8 am each morning for approximately 6 weeks beginning YYMMDD.

Research team people will send conferencing information to dial in.

The briefing will cover the hours from 11 am through 4 pm over the study region (described clearly here), including xx, yy, zz Counties.

The briefing will address general weather conditions, ceilings, visibility, wind speed and direction, and convective activity.

Ideal wind conditions are a prevailing wind at 4-8 m/sec.

Research team is particularly interested in whether the air mass is stagnant, fresh, or if a change in the air mass is expected.

Figure 2-61 shows a schematic overview of the sampling approach used for the regional mass balance emission estimation method. Typically, when winds are fairly consistent


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over the study region with a clear mean wind direction, a mass-balance flight starts by conducting an upwind transect in the PBL to document any PBL enhancement in the inflow from upwind methane sources. If time allows, a vertical profile is also conducted to get a sense of the PBL height and vertical mixing in the first hour of flight. The plane then flies the perimeter of the region until it reaches the downwind side of the region and starts conducting a series of downwind transects at different altitudes and/or distances from last study region sources interspersed - as flight plan and time allow – by one to three vertical profiles to measure the PBL height and document any spatial and temporal gradient. PBL height is also estimated from the wind profiler hourly scans and reported as a 5 min average at the location of the profiler

Figure 2-61: Sampling and measurement strategy for regional mass-balance method.

Under the right meteorological conditions (Table 2-8), the difference between methane in the PBL outflow and inflow of the study region is equal to the total regional emissions of methane.

The simplified analytical mass-balance equation we use to estimate the total regional methane emissions (mass of methane/hour) originating in the study area is:

𝐸(𝐶𝐻4) = 𝑀𝐶𝐻4. ∫ ∆𝑋𝐶𝐻4. 𝑉𝑐𝑜𝑠𝜃 𝑑𝑥 ∫ 𝑛𝑎𝑖𝑟𝑍𝑃𝐵𝐿𝐻

𝑧𝑔𝑟𝑜𝑢𝑛𝑑

𝑥2

𝑥1 𝑑𝑧 (1)

Where:

𝑀𝐶𝐻4 is the molar mass of methane (16 g/mole)


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𝑉 is the horizontal wind speed in the PBL over the study region for the time of the air mass transit over the region,

∆𝑋𝐶𝐻4 is the methane enhancement in the PBL along the downwind transect flown between markers x1 and x2 at relatively constant altitude –outside of the aircraft vertical profile and potential surface terrain complication –,

𝜃 is the angle between the mean wind direction and the direction perpendicular to the airplane track at any given point along the downwind transect,

∫ 𝑛𝑎𝑖𝑟𝑍𝑃𝐵𝐿𝐻

𝑧𝑔𝑟𝑜𝑢𝑛𝑑 𝑑𝑧 is the integral in the PBL of the molar density of air.

Table 2-15: Summary of measurements used for the mass-balance calculation

Variable symbol

Variable description Observations used Implementation notes

𝑉

10 sec moving average horizontal wind speed

In situ continuous at location of airplane

5-min average hourly from near surface to top of PBL above wind profiler location

Horizontal wind speed and direction averaged spatially (different aircraft transect locations), temporally (wind profiler time series), and throughout the vertical (wind profiler provides wind measurements at 60-100 m altitude levels within the PBL

𝜃

Angle between horizontal wind vector and direction perpendicular to flight track

Same as above

Allowed to vary along downwind track to adjust for plane heading compared to horizontal wind direction

∆𝑋𝐶𝐻4

Methane dry air mole fraction enhancement in PBL

Methane dry air mole fraction measured in PBL by airplane along mass-balance box flight track

High-resolution methane measured in PBL downwind plume minus mean or spatially resolved methane in PBL upwind of study region.

x1, x2

Location markers of the edges of the observed PBL methane plume along downwind transect

Zground Ground elevation


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Variable symbol

Variable description Observations used Implementation notes

ZPBL PBL height

Derived from observed gradients in temperature, water vapor and trace gas measurements during aircraft vertical profile and 5-min average hourly wind profiler data

nair Density of air in PBL

Derived using aircraft measurements of temperature and pressure

Study area airborne estimate for total methane emission

Meteorological conditions for the flights on October 1st and 2nd met the requirements for the area emissions mass-balance calculations. Both days were sunny, with no clouds and had strong and fairly uniform horizontal winds over the study area. Below we present the observations and data analysis used in the derivation of the study area airborne estimate for total methane emission.

October 1st, 2016

Figure 2-62: Time series of horizontal wind speed and direction from near the surface to 3-4 km above sea level. The time axis goes from right to left starting at 11pm UTC (6pm Local Time) on September 30, 2016.


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On October 1st, mean horizontal winds in the boundary layer were strong (7-8 m/s) and originating from 0-20 degrees North during the entire flight (red box in Figure 2-62 showing profiler data). Winds were consistently from the N to NNE (0-20 oN) with speeds over 8m/s for over 7 hours prior to the flight start time. Such strong winds guaranteed that surface emissions did not accumulate anywhere in the study region prior to the flight. The air mass sampled by the airplane was moving consistently over the region and accumulated surface emissions as it traveled from North to South over the shale gas extraction area. In-situ horizontal winds measured at 1Hz by the airplane show that the winds over the entire region were similar to what was measured by the profiler near Bee Branch (Figure 2-63).

Figure 2-63: Time series of horizontal wind speed and direction measured by the NOAA wind profiler near Bee Branch, AR. Lines represent for different altitudes (continuous) or altitude bands (dashed). Also shown with symbols are the mean horizontal wind speed and direction (and 1 SD uncertainties) measured by the airplane during the upwind transect, 2 downwind transects in the middle of the PBL, one downwind transect in the entrainment zone and one midfield transect conducted toward the end of the flight. On that day the profiler and aircraft measurements agree very well and show very consistent wind speed and wind direction in the PBL for the duration of the flight. These conditions ensure a very close to uniform sampling of point and area sources emissions by the air mass passing over the entire region on that day. With a mean wind of 7-8m/s, it took the air mass about 2.5 hours to transit through from north to south.

The airplane left from the Conway airport and headed east to cover the perimeter of the study area counter-clockwise and flying mostly in the boundary layer, below 1000 magl (Figure 2-64). The plane flew the upwind leg in the PBL from 12:20pm to 12:55pm LT at an average altitude of 340 magl. The plane conducted its first vertical profile at 12:07 pm LT to detect the PBL height and evaluate vertical mixing conditions in the PBL. The plane then flew the western leg heading south. It then flew three downwind legs with a spiral profile in the middle of each leg to probe the height of the PBL at different times, as PBL height is one of the critical variables in the equation for the mass balance emission calculation. Note that the PBL height kept rising throughout the day on


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October 1st and 2nd with varying growth rate in the morning and afternoon on October 1st, as shown in Figure 2-65.

Figure 2-64: October 1st flight track. Track (thin black line) is plotted over map showing locations of natural gas well pads (light blue dots), gathering (red dots) and transmission (dark blue dots) stations. Also shown with black arrows are 1-minute average wind speed and direction measured by the plane.

Figure 2-65: Observed temporal growth in the PBL near Bee Branch. Data from the NOAA wind profiler measurements for October 1st (red) and 2nd(green), 2016. Shaded boxes show the time intervals when the airplane was flying the upwind and downwind transects on each flight. Note the different times of the upwind and downwind transects and different PBL height growth patterns for the successive days.


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Figure 2-66: Aircraft measurements of virtual potential temperature, water vapor, dry air mole fractions of CO2 and methane during 4 vertical profiling spirals conducted on October 1st. The dash blue line and blue shaded area show the height of the PBL at the time and location of the profile. The measurements show a vertically well-mixed PBL on all four profiles. The structure observed during the profile is mostly due to spatial gradients encountered by the plane when spiraling upwards. The diameter of a profile spiral is typically ~2 km.

The CH4 enhancements used in the mass balance calculation are calculated from the difference between downwind transect CH4 mixing ratios (black and blue lines in Figure 2-67) and the background CH4 mixing ratios. The background CH4 values are based on the CH4 measurements upwind (red line in Figure 2-67), which represents an explicit and spatially resolved estimate of CH4 inflow into the study region. The spatially varying CH4 background was determined by shifting the upwind CH4 mixing ratios based on wind direction and speed as well as distance between upwind and downwind transects. This technique for determining spatially resolved background CH4 mixing ratios assumes that the measured upwind structure was transported through the study area, thereby influencing the downwind CH4 mixing ratios. The downwind 2 Western edge CH4 mixing ratios are smaller than those of the downwind 2 Eastern edge and both edges of downwind 1. We attribute this spatial difference in CH4 dilution to a longitudinal


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gradient in the PBL growth between downwind 1 and 2, and its associated entrainment of air from the free troposphere (see both figures above).

Figure 2-67: Methane dry air mole fraction measured with Picarro instrument along the aircraft upwind transect (red line) and two downwind transects flown in the middle of the PBL. The red dashed line indicated the mean methane value in the upwind transect. Note that the PBL was rising throughout the day. Note also that the observed downwind enhancements are of the order of 0-40 ppb, which can only be detected reliably with adequate instrumentation and calibration. Downwind transect 1 (black line) was flown west to east between 13 and 13.9 LT. Downwind transect 2 (blue line) was flown east to west between 14 and 14.9 LT, at a mean altitude 200m higher than downwind 1.

To calculate the study area total methane emissions, we use the equation presented in 2.3.2 and the measurements presented above as summarized below. Table 2-16 below lists the observations –mean values and mean uncertainties- used in the calculation of the total mean methane emissions and the associated uncertainty for each downwind transect.


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Table 2-16: Summary of the mean observed values for the key atmospheric variables in the mass-balance equation for the October 1st flight analysis. The last row shows the standard error of the means of both downwind transect estimates.

The aircraft based study area total methane emission estimate for the midday hours on October 1 is

29.3 ± 13.0 Mg/hr (mean and 2-sigma confidence interval) for the first downwind and 28.1 ± 11.4 Mg/hr for the second downwind –conducted 1 hour apart. The mean emission estimate for the two downwind legs on 10/1 is 28.7 ± 8.6 Mg/hr (2-sigma).

October 2nd, 2016

On October 2nd 2016, the airplane conducted a raster flight with multiple west-east legs from north to south. Horizontal winds were from NNE with an average speed of 7m/s.


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Figure 2-68: Similar to Figure 2-64 above for October 2nd flight

During this flight, no vertical profile was conducted, so we use the profiler PBL height data in the mass balance equation for the two southernmost and complete west-east downwind legs.

Figure 2-69: Similar to Figure 2-67 for the October 2nd flight

Table 2-14 below lists the observations –mean values and mean uncertainties- used in the calculation of the total mean methane emissions and the associated uncertainty for each downwind transect on October 2nd.


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Table 2-17 Similar to Table 2-16 for October 2, 2015

The aircraft based study area total methane emission estimate for the midday hours on October 2 is 36.4 ± 22.6 Mg/hr (mean and 2-sigma confidence interval) for the first downwind and 37.0 ± 20.7 Mg/hr for the second downwind –conducted 1 hour apart. The two downwind legs give very similar results. The mean emission estimate for the two downwind legs on 10/2 is 36.7 ± 15.4 Mg/hr (2-sigma). This estimate is larger than the mean emissions derived for October 1st. It also has a larger uncertainty due mostly to the slightly larger variability in the horizontal winds and larger uncertainty PBL height, only measured by the profiler, on 10/2.

The estimated CH4 emissions including uncertainties as a function of longitude are shown in the figure below (panel a). Given the differences in the mean wind speeds (2° N and 17° N on Oct 1 and Oct 2, respectively), the longitudinal CH4 emissions on both days refer to slightly different NG infrastructure footprints sampled as illustrated in panel b. However, the mutual natural gas infrastructure accounts for the vast majority of the footprint for both days. Note the similarities of the longitudinal CH4 emission features between both days as expected in the absence of significant changes in NG industry operations and non-NG sources. The greatest and smallest emissions occurred near -92.5° and -92.0° longitude, respectively.


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Figure 2-70: Calculated emissions for aircraft mass balance. Panel a) Study area spatial (longitudinal) distribution of CH4 emissions for each 0.02° longitude interval (shaded areas represent 1 SD uncertainties) for October 1 and October 2 corrected for wind direction from Panel b). Panel b) The parallelograms for October 1 and October 2 show the slightly different NG infrastructure footprints covered by the downwind transects on those days. The angle of these parallelograms is consistent with prevailing wind direction on those days. The overlap of both parallelograms illustrates the mutual natural gas infrastructure “sampled” in the spatial CH4 emission estimates of both days. The dashed rectangle shows the footprint during a hypothetical flight with 0° N wind direction for reference.

Influence of upwind CH4 structure

We analyzed the influence of including the upwind CH4 structure in the CH4 emission calculation on the results on both flight days as shown in Figure 2-71 Panel a) illustrates that total study area CH4 emissions could be biased by up to 30% a constant background


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CH4 value based on the downwind transect edges were used. On October 1, failing to account for the upwind structure results in 20-70% lower CH4 emissions compared to including the upwind structure in both the Western and Eastern half of the study area. As shown in panel b), accounting for the upwind structure leads to better agreement in CH4 emissions in both the Western and Eastern half of the study area on these two consecutive days.

Figure 2-71: Sensitivity analysis of emission estimate to background CH4 treatment. Panel a) Changes in CH4 emissions if a constant background CH4 value based on the downwind transect edges were used instead of spatially varying CH4 background values (this study). Panel b) CH4 emission difference between October 1 and October 2 flights using both methods of background treatment. We divided the Western and Eastern half of the study area at 92.1° longitude.

2.7.2 Emissions Attribution

2.7.2.1 Concept and Model

CH4 can be emitted by multiple source processes besides fossil fuel operations including ruminants, landfills, wastewater treatment plants, natural wetlands and inundated rice fields. These biogenic sources can emit methane but they do no emit ethane, C2H6.


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Table 2-18: Methane and ethane sources in the study area

Source CH4 C2H6

Natural gas operations and infrastructure. Also potentially natural gas micro-seepage

X X (varying amounts)

Cattle X

Poultry manure X

Landfills X

Waste Water X

Wetlands X

The aircraft mass-balance method estimates total methane emissions for the study area. For this project, we need to isolate the emission contribution from natural gas operations alone. Previous methane mass-balance studies have used bottom-up information to constrain non-oil and gas source emissions [4-6]. For the Barnett Shale coordinated study, Smith et al. [21] analyzed ethane and methane plumes from one flight in the PBL over the Barnett gas producing core region on a day with very low wind speed and larger atmospheric signals. Based on the distributions of C2H6-to-CH4 enhancement ratio of observed small-scale plumes, Smith et al. picked a threshold of 0.8% to differentiate biogenic (< 0.8%) from thermogenic (>0.8%) CH4 sources.

In this study we use a top-down approach described below. We present in the next sub-sections the observations and data analysis leading to the study area CH4 emissions attribution between natural gas related sources versus other sources.

For an area of interest (study area or portion of study area), we separate emission contributions from natural gas sources E()NG and other sources E()other:

𝐸(𝐶𝐻4)𝐴𝑟𝑒𝑎=𝐸(𝐶𝐻4)𝑁𝐺 + 𝐸(𝐶𝐻4)𝑜𝑡ℎ𝑒𝑟 (1)

𝐸(𝐶2𝐻6)𝐴𝑟𝑒𝑎=𝐸(𝐶2𝐻6)𝑁𝐺 + 𝐸(𝐶2𝐻6)𝑜𝑡ℎ𝑒𝑟 (2)

Contributions of C2H6 from other known major sources, such as biomass and biofuel burning, are assumed to be negligible within the study area. There was a fire ban in Cleburne, Conway, and Faulkner counties at the time of the study (Arkansas Forestry Commission, personal communication, 11/11/2016).

Therefore,

𝐸(𝐶2𝐻6)𝑜𝑡ℎ𝑒𝑟~0 (3)

𝐸(𝐶2𝐻6)𝐴𝑟𝑒𝑎=𝐸(𝐶2𝐻6)𝑁𝐺 (4)


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We use the aircraft data to constrain the ethane to methane emission ratio [𝐶2𝐻6

𝐶𝐻4]

𝑎𝑟𝑒𝑎

for all sources in the area and the Mobile Labs plume data to constrain natural gas

sources related emission ratio [𝐶2𝐻6

𝐶𝐻4]

𝑁𝐺for the area of interest.

We use all available data from the campaign to derive estimates of the two emission ratios above. Then, we solve for the relative contribution of CH4 from natural gas sources in the area of interest with equation (5) below. This calculation is independent of the total area CH4 emission estimate. The result represents an average fraction for the time of the campaign.

𝐶𝐻4𝑁𝐺

𝐶𝐻4𝑡𝑜𝑡𝑎𝑙=

[𝐶2𝐻6

𝐶𝐻4]

𝑎𝑟𝑒𝑎

[𝐶2𝐻6

𝐶𝐻4]

𝑁𝐺

⁄ (5)

2.7.2.2 Methane and non-methane hydrocarbon measurements

Flask measurements

Methane and C2-6 alkanes (ethane to n-hexane) dry air mole fractions were measured in discrete air samples collected by the aircraft and the NOAA van at the NOAA Boulder laboratories. In the field, air samples were collected in individual facility CH4 emission plumes as well as in local background and randomly in the study region.

The next two figures show analysis results for the aircraft and surface Mobile Lab discrete air samples.

The surface sample data collected by the NOAA van show a range of 0-8 ppm for CH4 enhancement above background and 0-170 ppb for C2H6. A linear fit through the mobile lab data gives a slope of 1.3% C2H6/CH4.

The aircraft sample data span a smaller range as emission plumes become more diluted by the time they reach the aircraft at altitude in the boundary layer: 0-<0.4 ppm for CH4 and 0-7 ppb for C2H6. 96 out of 105 samples were collected between 300 and 1000 meters above sea level (masl).


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Figure 2-72: CH4 and C2H6 dry air mole fractions measured in the NOAA flasks collected by both the NOAA van (green symbols) and the aircraft (blue symbols).

Figure 2-73: Same plot as above but zoomed in.

The natural gas produced in the study region is labeled as “dry gas” as it has low volatile organic compound (VOC) content. Figure 2-74 and Figure 2-75 below show scatterplots of propane (C3H8) versus C2H6 and n-butane (nC4H10) versus (iC4H10) for 91 aircraft samples with valid chemical analysis. The two air samples with the lowest VOC mixing ratios were collected in the free troposphere (above the boundary layer) at 2.4 and 2.5 km altitude above sea level. The butanes show a very tight correlation with a slope of 2.1 ppt/ppt.


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Figure 2-74: C3H8 versus C2H6 dry air mole fractions measured in the NOAA flasks collected by the aircraft.

Figure 2-75: nC4H10 versus iC4H10 dry air mole fractions measured in the NOAA flasks collected by the aircraft. The average nC4H10 –to- iC4H10 enhancement ratio is 2.1.

2.7.2.3 Evaluation of NOAA van in-situ CH4 and C2H6 measurements

Instrument calibration

The Picarro CH4/CO2/CO/H2O and Aerodyne C2H6/CH4/H2O analyzers were calibrated prior to and at the conclusion of each mobile laboratory drive using a three-point calibration. Three cylinders containing “whole air” spiked with CH4 and C2H6 with resulting mole fractions ranging from 1862 to 2200 ppb and 2.040 to 19.96 ppb, respectively, were used.


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Using calibration curves, calibration factors of 0.94 and 0.95 were determined for the Aerodyne analyzer in situ CH4 and C2H6 data, which suggests that CH4 and C2H6 mole fractions were underestimated by 6 and 5%, respectively. A correction was applied but no additional calibration adjustments were made because the Aerodyne instrument data were only used to calculate relative C2H6/CH4 ratios as opposed to absolute C2H6

and CH4 values.

Fast response in-situ CH4 measurements displayed in real time for operators on a screen in the front of the van are essential to locate CH4 emission plumes and to guide the instantaneous air sampling of NOAA surface flasks. Figure shows an example of in-situ CH4 and C2H6 mixing ratios measured downwind of a well pad.

Figure 2-76: Time series of CH4 and C2H6 dry air mole fractions in-situ measurements from the NOAA van while mobile. Measurements made with the new Aerodyne analyzer downwind of a well pad with detectable emissions.

Figure 2-77: Time series of CH4 and C2H6 dry air mole fractions in-situ measurements from the NOAA van while parked and stationary downwind of the site.


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Methane plumes, like those shown above, were defined by a 50 ppb or higher enhancement. Using tools written by Aerodyne Research, Inc., C2H6 and CH4 data points in each plume were fit with a line using ordinary least squares regression and an C2H6/CH4 slope was determined.

Figure 2-78: Data from a methane plume fit with a line to obtain a slope of 1.3%.

Evaluation using flask measurements

To evaluate the CH4 and C2H6 enhancement ratios derived from the Aerodyne analyzer in-situ measurements, we collected multiple flasks in different single facility emission plumes and compare the slope derived from the flask sample data and the in-situ measurements. Figure 2-79 is an example of such a comparison for a well pad. The two dataset slopes (1.3%) agree.


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Figure 2-79: Scatterplot of C2H6 versus CH4 for the in-situ measurements with the Aerodyne analyzer (black circles) and the NOAA flasks (red diamond) downwind of a well pad with detectable emissions.

Figure 2-80 shows another example of a single facility C2H6 to CH4 slope comparison. This time data was collected downwind of a gathering compressor station. Again the two dataset slopes (1.4%) agree.

Figure 2-80: Scatterplot of C2H6 versus CH4 for the in-situ measurements. Aerodyne analyzer (black circles) and the NOAA flasks (red diamonds) taken downwind of a well pad with detectable emissions (same in-situ data as time series shown in previous figure).

Figure 2-81 below shows a comparison of the flask and in-situ C2H6 to CH4 slopes for plumes detected downwind of two well pads and five compressor stations. The in-situ


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data may consist of multiple plume transects and individual slopes were derived for each one. A time-averaged slope for the in-situ plumes is shown by the symbol and the error bar shows the standard deviation around the average.

Figure 2-81: Comparison of in-situ and flask C2H6 to CH4 slopes in plumes from seven different facilities in the study area.

C2H6 to CH4 ODR slopes in NG facility emission plumes from NOAA and Aerodyne drives

In-situ 0.5 Hz Picarro CH4 data from 11 drives by the NOAA lab were analyzed to systematically investigate C2H6 to CH4 correlation (and if correlated, C2H6 to CH4 correlation slopes) in CH4 plumes with mixing ratio enhancements 50 ppb or more above the local CH4 background mixing ratio. Plumes from 79 NG facilities passed the filtering criteria. While a correlation coefficient (R2) of at least 0.65 was set as a requirement for the ethane to methane slope to be included in this analysis, 85% of the NG plumes included had R2 > 0.95.

We used an orthogonal distance regression code in Igor to calculate the slope of the linear fit between C2H6 to CH4 in plumes meeting the criteria. In-situ measurement uncertainties of 2 ppb for CH4 and 100 ppt for C2H6 are used in the regression.

To increase the number of plume ratio we use for the attribution we combine data from the NOAA and Aerodyne vans. The map in Figure 2-82 shows where the two teams collected ethane to methane enhancement ratios in the study area. Note that the Western portion of the study area with a denser network of public roads and a larger number of survey drives with the NOAA van has more ratios.


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Figure 2-82: Location of ground level NG plume measurements. Data shown with NOAA Mobile Laboratory (circle) and Aerodyne (square) vans. The color of the symbol shows the measured ethane to methane enhancement ratio.

Figure 2-83 below shows the distribution of the ground NG emission plume ratios for the NOAA and Aerodyne vans.

Figure 2-83: Probability distribution of C2H6 to CH4 ODR slopes for measured emission plumes from 78 facilities in the study region.


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Mobile Labs data for western vs. eastern portions of study area

Similarly to the aircraft mass-balance analysis, we separate the results for facility levels C2H6 to CH4 ODR slopes between the western and eastern halves of the study region. The boundary between the two halves is set at 92.1 oW longitude.

Also to increase the number of facilities with C2H6 to CH4 ODR slopes for our analysis, we include C2H6 to CH4 ODR slopes reported by the Aerodyne team for the dual tracer facility-level emission quantification effort. Sites sampled by both the NOAA and Aerodyne mobile laboratories show agreement in C2H6 to CH4 slopes.

Figure 2-84 below shows the distribution of the C2H6 to CH4 ODR slopes for the merged NOAA and Aerodyne facility plume results for the western and eastern halves of the study region. The majority of NG facility plumes sampled by both teams had slopes between 1.0 and 1.7%. However, both teams report slopes higher than 2% for 11 emitting facilities in the eastern half of the study region.

Figure 2-84: Probability distributions of C2H6 to CH4 ODR slopes for measured emission plumes from 78 facilities in the Western and Eastern halves of the study region.

Signals from the aircraft are a lot smaller than what was detected on the ground by the van when downwind and in proximity to emitting sources. The Aerodyne instrument noise (a few 100s ppt for ethane) and the smaller size of the enhancements detected in


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the PBL by the aircraft makes the analysis of the aircraft data more challenging than for the ground data.

2.7.2.4 Calculation of representative natural gas C2H6 to CH4 ratio from mobile laboratory data

In order to determine a natural gas sources mean emission ratio ([C2H6/CH4]NG) for the full study area, Western portion, and Eastern portion, observed plume ratios are weighted by gas production at nearby facilities and a production weighted average is calculated. This is accomplished using the method described below.

The study area is first divided into a set of triangles using Delaunay Triangulation as coded in MATLAB. This method connects a set of points (in this case, measured C2H6/CH4 ratios and their associated latitudes and longitudes) so that no other point lies in the circumcircle of a given triangle and the angles of each triangle are maximized. This method of dividing the study area was chosen over a grid because the varying sizes of the triangles account for variability in measurement density and ensure that there are no triangles that contain no data. Dividing the entire study area using mobile laboratory measurements and Delaunay Triangulation divides the study area as shown in Figure 2-85.

Figure 2-85:The study area is divided into triangles by connecting locations where mobile laboratory natural gas C2H6/CH4 ratios were obtained using Delaunay Triangulation.

The three vertices of a triangle are each a measured C2H6/CH4 ratio. For each triangle, the three enhancement ratios are averaged and that average is assigned as the ratio for


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the triangle. Using data provided by partners in the project, the production of all production pads contained within each triangle is summed. Using the C2H6/CH4 ratio for each triangle and sum of production within, a weighted average C2H6/CH4 is calculated for the study area. Applying the above method to the Eastern and Western portions of the study area results in divisions as shown in Figure 2-86.

Figure 2-86: Triangulation of the Western portion of the study area (left) and Eastern portion (right) with all observed NG plumes ratios.

To evaluate each half region mean ratio and standard deviation, a Monte Carlo simulation is run in which a random set of 20% of the data points is removed and the weighted C2H6/CH4 is calculated. The MATLAB code is run 30 times and an average ratio and standard deviation are calculated. The simulation yields results summarized in the table below.

Table 2-19: Average natural gas C2H6/CH4 ratio for each study area region as determined by Delaunay triangulation followed by a Monte Carlo simulation.

Our attribution model assumes that the mean emission ratio does not change from hour-to-hour and day-to-day. The area averaged natural gas emission plume ratios

Region Natural gas plumes mean

C2H6/CH4

Full study area 1.60 ± 0.09%

Western portion

1.34% ± 0.06

Eastern portion

2.00 ± 0.14%


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above are still preliminary. To get a first order estimate for the attribution, we use these numbers in the attribution model equations presented earlier.

2.7.2.5 Calculation of C2H6 to CH4 total emission ratios from aircraft data

We use the aircraft in-situ data to determine area averaged C2H6/CH4 emission ratio. The two flights used for the methane emission mass-balance calculations had relatively high wind speeds in the PBL (~ 8m/s), leading to lower atmospheric signals. The data on those two flights cannot be used for this analysis due to low signal to noise ratio for the ethane measurements.

An example is shown here (Figure 2-87), in which the full field downwind aircraft transect on the October 2nd flight has an ethane signal to noise ratio that is too low (< 5

) for use in an ethane mass balance calculation. That transect was used for the methane emissions mass balance calculation as the in-situ methane measurements had a much better signal to noise.

Figure 2-87: CH4 (red) and C2H6 (green) on the last downwind aircraft transect for the October 2nd, 2015 flight, as a function of longitude.

Similar to the mobile laboratory in-situ data, ethane is plotted against methane and a slope is calculated. Orthogonal distance regression is used and weighted by the noise of each of the instruments (100 ppt for C2H6 and 1.3 ppb for CH4). ). Below is an example of this approach using a leg (leg 9) during a raster flight that took place on October 5, 2015 (Figure 2-88).


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Figure 2-88: A raster flight of the Western portion of the study area conducted on October 5, 2015 is used to obtain Western C2H6/CH4 area ratios. The example leg, leg 9 is labeled on the top map. Arrows on the bottom-up depict the horizontal wind speed and direction along the flight track. The mean wind speed for the entire flight was fairly low at less than 3m/s.


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The CH4 enhancement is identified by examining CH4 data from the entire leg plotted as a function of longitude or latitude, depending on the flight pattern. In this case, latitude was chosen.

Figure 2-89: CH4 and C2H6 measured during leg 9 of the October 5 flight. The black box indicates the region of the study area in which CH4 is enhanced.

The ethane to methane slope is calculated for the portion of the leg with enhanced CH4.

Figure 2-90: An orthogonal distance regression fit to data from a raster conducted over the Western portion of the study area yields a Western C2H6/CH4 ratio of 1.1 ± 0.02. The average ratio for all legs on this flight that met criteria for acceptance was 1.2%.

Criteria for accepting or rejecting the result of the above analysis included the requirement that the C2H6 enhancement be at least 1 ppb above the background, a


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coefficient of determination (R2) of at least 0.65, that the measurements were taken in the planetary boundary layer, and that the flight was not conducted on a cloudy day (therefore no large scale convection).

For flights for which multiple downwind transects or legs fit the above criteria, the resulting C2H6/CH4 ratios were averaged. The following are preliminary aircraft ratios:

Table 2-20: Aircraft C2H6/CH4 ratios resulting from multiple PBL transects.

Date Type of flight C2H6/CH4 (in situ)

10/2/2015 Regional Raster (West) 1.1 ±0.04%



CH4 plumes encountered in the Western region had more consistent ratios. Additionally, plumes observed in the Western region were seen rather consistently throughout legs of raster flights. Plumes on flight legs over the Eastern half of the study area have greater variability in the ethane to methane ratio.

At this point in the data analysis, we have not been able to finalize the top-down attribution for the Eastern portion of the basin where emissions have been shown to be much lower, leading to lower atmospheric signal to noise in the measurements.

2.7.2.6 Attribution of methane between natural gas and other sources

The following values or range of values are used in Equation 5.

𝐶𝐻4𝑁𝐺

𝐶𝐻4𝑡𝑜𝑡𝑎𝑙=

[𝐶2𝐻6𝐶𝐻4 ]

𝑎𝑟𝑒𝑎

[𝐶2𝐻6𝐶𝐻4 ]

𝑁𝐺

⁄

Table 2-21 : Values used in Equation 7 to apportion CH4 emissions in the Western portion to natural gas and non-natural gas sources

Western portion

[C2H6/CH4]area = 1.2 ± 0.1%

[C2H6/CH4]NG = 1.34 ± 0.06%

Dividing the average area and natural gas ratios (1.2% and 1.34%, respectively), yields 90%. The uncertainty on each ratio (0.1% for area and 0.06% for natural gas) is propagated to obtain an uncertainty estimate for the relative emission contribution of natural gas versus non natural gas sources of 12%.


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The mean estimate of the portion of total CH4 emissions in the Western half of the field originating from natural gas sources is estimated to be 90 ± 12% and the estimate for the portion of total CH4 emissions originating from other sources is estimated to be 10 ± 12%.

We could not derive a comparable top-down estimate for the Eastern half on the study area with the current analysis method.


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3 Basin-Scale Modeling

3.1 Activity estimates utilized for basin-level analysis

A ground-level area estimate (GLAE) of emissions was developed from measurements, external sources, and engineering analysis. This model was spatially and temporally resolved to aid in comparison to aircraft estimates for the entire study area. The spatial data sources utilized in the model are shown in Figure 3-1, and paragraphs below discuss architectural details of the model utilizing the numbered items in the figure.

Figure 3-1: Spatial activity data for the ground-level area estimate of emissions

Specific spatial models:

Block (1): As described in Section 2.3.2 and Figure 2-7, well locations are well known, but emissions for this study were modeled primarily at the well pad “facility”

(1)Well

Locations

(3)Gathering Compressor

Station Location

(2)Gathering

Pipeline Length Estimate

(6)County Estimates of

Agricultural Methane Emissions

(4)Transmission Facility

Location

(5)Distribution System

Locations

(7)Geologic Seepage

Estimate for Study Area

Emission Source

Location Model

(1)Well Pad Grouping


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level. Well location data was available from the Arkansas Oil & Gas Commission (AOGC) web site prior to the field campaign, and these data were updated with additional confidential data from the study partners to refine the count, status and location of wells. Well pads were identified by grouping wells into well pads, as described in Section 2.3.2. From the analysis in Section 2.5.1 and [22], and in particular the comparison with downwind measurements made using tracer techniques, there is substantial confidence in the emissions modeling for individual well pads. These emission models utilize component counts – from partners or estimated – to develop facility-scale emissions estimates for well pads. Therefore, while emission locations were resolved to a well pad location, emission sources were developed for individual devices, attached to each well, on the well pad.

Block (2): Gathering pipeline lengths are based upon extensive partner and non-partner data for pipeline length and pipeline material type, as described in Annex 7. Auxiliary component counts were developed utilizing both partner data and satellite imagery. However, location of the pipeline was not well known for the model. To estimate the amount of pipeline, the team assumed that the well pad count was a sufficiently accurate surrogate for pipeline length in sub-areas of the study area. Pipelines and associated auxiliary equipment were distributed across the study area in proportion to the number of well pads.

Block (3): Study partners provided the location of gathering compressor stations, and in most cases, a description of the equipment at the facility (e.g. number of compressors, type of compressor driver, etc.). Facility emission models could then be assigned to specific locations in the study area.

Block (4): Transmission compressor stations were handled similarly to gathering compressor stations. Due to good participation by transmission companies Kinder Morgan and Enable Gas Transmission, the location of virtually all major transmission facilities in the study area was known and included in the GLAE.

Block (5): Distribution emissions were spatially aligned with distribution equipment locations by County. Functional locations provided by partner are not coordinate based, therefore accurate spatial modeling is not possible. Additional details are provided in Annex 8.

Block (6): Agricultural emissions made heavy use of reported plant and animal information from the US Department of Agriculture as well as emissions factors and classification schema from EPA’s greenhouse gas inventory. Since the bulk of this information was available at the county level, activity emissions estimates


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were developed at a county level, assuming a uniform distribution of emission sources across the county. An exception to this methodology is wetlands, where emission locations could be further isolated using maps of wetland areas. Annex 9 provides more detail on both data sources and modeling methodology. The spatial modeling of non-O&G emissions were then allocated to sub-areas of the study area intersection the sub-area with the county or counties (or wetland area) where emissions occurred.

Block (7): Geological seepage was handled similarly to agricultural emissions in (6), although data sources for emission rate, source type, and source distribution were acquired from peer-reviewed literature on seepage (see Annex 9). During post-campaign quality control meetings, the study team learned that some communities in the study area had utilized shallow methane for streetlights and electricity generation (albeit at a small scale) in the early 20th century. This raised the possibility that there may be, or may have been, localized, higher-rate, geologic seeps in the study area. An extensive search was made of public records, and while reference to usage could be found, no specific locations could be identified. Operational employees of partner companies – most of whom are long-term residents of the study area – were queried extensively about any known methane seeps in the area (none were reported), and about past usage of shallow methane (some specific location were mentioned, but no additional data could be located). Based upon this investigation and discussion with other experts in geologic seepage, the study team decided to utilize a model that (a) uniformly distributed geologic seepage of the area defined by the Fayetteville Shale play, and (b) publically-available data for emission rates that was suitable for distributed ground seepage. References and additional discussion is provided in Annex 9.

In addition to the spatial model, certain large episodic emissions have a significant impact on the temporal variability of emissions. Several such emission sources include:

Manual liquid unloading operations for wells. In the study area, these unloadings are clearing water, not liquid hydrocarbons, from the well bore. While automatic unloadings are triggered by on-site controllers, manual unloadings are initiated by operators. As a result, manual unloadings typically happen during the work day, and since many unloadings take several hours, operators are more likely to initiate these events during the morning hours. The timing and mix of unloading events is discussed in detail in [22] and in Annex 5.


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Blow-downs occur when pressurized gas in compressors (unit blowdowns) or yard piping (facility blowdowns) is released to the atmosphere to depressurize components. Compressor blowdowns may happen under remote control at any time, or may be initiated for maintenance activities by on-site personnel. For this study, excellent cooperation by partner companies (both funding and non-funding) allowed the study team to develop a complete list of blowdowns. The exact timing of the blowdown operation was not always known, and was modeled as indicated in Vaughn, et. al. [18], [23].

Due to low gas prices in the fall of 2015, few new wells were being drilled or completed in the basin. However, exploration activities may account for large episodic emissions during flow-back, drilling out hydraulic fracturing plugs, and similar operations. The study team worked with study partners to identify when and where this type of emission occurred. While times were not always precise, windows of time were identified, along with type of activity occurring during the window. As discussed in [18], one such drill-out operation occurred near a compressor station during measurement and was identified by an attempted aircraft facility measurement. (Emissions from the operation were not measured by the aircraft.)

In addition to the major episodic emissions mentioned above, numerous gas pneumatic devices operate in an intermittent – that is episodic – fashion. Due to the large number and wide spatial distribution of these sources, emissions were modeled as time-averaged emission rates spatially resolved to the facility where they occurred.

Using the methods above, the GLAE is resolved in one-hour time intervals, and emissions are either distributed over county-sized area emissions or are assigned to specific spatial locations. During Monte Carlo simulation, modeling results are consolidated at the facility and basin scales, and are also assigned to grid squares to provide spatial resolution.

3.2 Methods to compare study area emission estimates

The aircraft mass balance (AMB) estimate of emissions in the study area produces results which are solved at the edge of the mass balance “box” flown by the aircraft. This box encompasses all methane emission sources within the study area. For this project, all mass balance estimates occurred during periods with northerly winds, with mean wind directions on both days between 2°N and 17°N. The wind direction places


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the methane flux estimate from the method along the southern edge of the study area between 35.1° and 35.2° north latitude [24].

To compare estimates, emissions ground level area estimates (GLAE) must be mapped from the spatially-distributed GLAE model to the location of the AMB results. Individual emission points in the GLAE were propagated downwind utilizing a Gaussian dispersion model, as described in [23]. An example is shown in Figure 3-2. Each individual source is propagated to the southern transect used by the aircraft mass balance flights, and emissions from each source are summed into a spatially resolved profile along the transect line. A schematic representation of this process is shown in Figure 3-3. Consolidation of emissions is done using distance bins along the transect line, as shown in the figure.

Figure 3-2: Typical Gaussian plume model.


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Figure 3-3: Schematic of spatially shifting emissions to a location comparable with aircraft methods.

To handle the temporal resolution of the GLAE, emissions are calculated at the transect line based upon what was happening at each emission point, considering the transport time from the emission point to the transect line. Wind speeds for transport were assumed to be average wind speeds measured during the aircraft flights. Note that the timing of emissions are resolved no finer than the 1-hour resolution of the GLAE.

3.3 Ground Level Area Estimate and Aircraft Mass Balance Comparison

Ground level area estimates (GLAEs) attempt to account for all methane emission sources within the study area, including natural gas operations from all industry segments, and several non-oil and gas sources, as described in Annex 9: Non-O&G Study Area Model. GLAEs are compared to aircraft mass balance estimates at the approximate time the aircraft made downwind transects of the study area on October 1, 2015 and October 2, 2015. As shown in Figure 3-4 and Table 3-1, on October 1 the aircraft estimated a study area total methane emission rate of 28.7 Mg/h, while the GLAE was 22.4 Mg/h. The box and whiskers for the aircraft data point represent one and two standard deviations, respectively. The box and whiskers for the GLAE represent one standard deviation and empirical 95% confidence intervals from the Monte Carlo model, respectively.


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Figure 3-4: Aircraft Mass Balance and Ground Level Area Estimate on October 1, 2015. The GLAE shown represents emissions occurring 1-2 pm CDT.

Table 3-1: Ground level area estimate and aircraft mass balance results for October 1, 2015.

As shown in Figure 3-5 and Table 3-2, on October 2nd the aircraft estimated a study area methane emission rate of 36.7 Mg/h, while the GLAE was 24.2 Mg/h. The box and whiskers for the aircraft data point represent one and two standard deviations, respectively. The box and whiskers for the GLAE represent one standard deviation and empirical 95% confidence intervals from the Monte Carlo model.

Figure 3-5: Aircraft Mass Balance and Ground Level Area Estimate on October 2, 2015. The GLAE shown represents emissions occurring 2-3 pm CDT.

Table 3-2: Ground level area estimate and aircraft mass balance results for October 2, 2015.

Oct 2 Mean (Mg/h) 1 sigma 95% CI or 2 sigma

GLAE 24.2 ± 1.8 +3.7/-3.5

AMB 36.7 ± 7.7 ± 15.4

Oct 1 Mean (Mg/h) 1 sigma 95% CI or 2 sigma

GLAE 22.4 ± 1.8 +3.7/-3.5

AMB 28.7 ± 4.3 ± 8.6


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Qualitatively, the GLAE and aircraft mass balance agree more closely on October 1st than October 2nd; however, confidence intervals for the GLAE and aircraft mass balance overlap on both October 1st and October 2nd. This result indicates statistically similar total study area methane emissions estimates from completely independent top-down and bottom-up methods on two consecutive days.


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4 Answers to Study Key Questions

This chapter considers the study questions outlined in the project Statement of Project Objectives, and discusses conclusions from each question in turn.

4.1 What portion of total basin methane emissions can be attributed to oil and gas industry sources?

The Ground Level Area Estimate considers CH4 emissions from all sectors of the natural gas supply chain that exist within the study area boundaries, as well as CH4 emissions from livestock, wetlands, landfills and geologic seeps. The average CH4 emission rate predicted by the GLAE for the two-day period spanning October 1st and 2nd, 2015 16.3 Mg/h (-2.3 / +2.6 Mg/h; 95% confidence interval reported for all GLAE estimates throughout the report). Non-oil and gas sources contribute 3.7 Mg/h (-1.1 / +1.4 Mg/h) to the GLAE, therefore approximately 77% of CH4 emissions predicted by the GLAE for the study area can be attributed to oil and gas operations. Conversely, 23% of the GLAE can be attributed to non-oil and gas sources.

For the western portion of the study area, the GLAE mean emission estimates for natural gas related methane sources versus other sources are 9.1 Mg/h (-1.6 / +1.9 Mg/h) and 2.2 Mg/h (-0.7 / +0.9 Mg/h) respectively. Therefore, for the western portion, GLAE attributes 81% (-7 / + 9 % ) of the methane emissions to natural gas sources. For comparison, the top-down attribution model estimates that natural gas sources contributed 90% ± 12% (2 sigma) of total methane emissions in the western portion (section 2.7.2.6). The atmosphere-based and inventory based estimates of the fraction of emissions attributable to natural gas sources agree well for the Western portion of the study area.

4.2 What portion of total basin methane emissions from oil and gas operations is contributed by large emission sources?

As shown in Figure 4-1 below, only 2 gathering stations out of the 30 measured showed throughput normalized emissions greater than 1%, based on tracer measurements. Malfunction and maintenance actions were identified as the cause for “higher than typical” emissions at each of these facilities. No similar exceptional conditions were noted at any other measured gathering stations. For this reason, gathering stations with throughput normalized emissions greater than 1% are considered large emitters in this study.

The facility undergoing maintenance was identified by the aircraft measurement team during a raster flight. Onsite and tracer measurement teams were dispatched to corroborate the aircraft measurements and identify the cause. Since the facility was not


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selected by ground-based measurement teams during by random sampling, it is self-represented during the GLAE scale-up.

The second gathering station with throughput normalized emissions greater than 1%, was one of the stations selected randomly for ground measurement, and showed excessive tank venting due to an unidentifiable malfunction. We estimate the emissions from the tank by taking the difference between the tracer facility estimate and the SOE, and apply the uncertainty of the tracer measurement to this difference. When scaling to the GLAE we assume that this type of malfunction and resulting emission occurs at the observed frequency, and estimated magnitude.

Figure 4-1: Throughput normalized emissions at gathering stations measured by downwind tracer.

Vented liquid unloadings are the dominate emission source at production well pads (manual or automatic, plunger or non-plunger), accounting for 84% of the average emission rate from the production sector for the two-day period spanning October 1st and 2nd as simulated in the GLAE model. Unloading emissions were calculated as described in Annex 5: Production SOE Development. Briefly, study partners provided unloading activity data, and rates were estimated using Monte Carlo methods and mid-continent liquid unloading measurements from Allen et al. [25].

Sources from large emitters for these two facility categories, when scaled to the study area population in the GLAE model, contribute 6.3 Mg/h (-1.8 / +2.1 Mg/h) to the GLAE. The average CH4 emission rate predicted by the GLAE for natural gas sources for the two-day period spanning October 1st and 2nd, 2015 is 12.0 Mg/h (-1.8 / +2.1 Mg/h). Therefore, approximately 53% of natural gas emissions are due to large emission sources, on average, for the two-day period during which the aircraft mass balance flights occurred.


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4.3 What portion of total basin O&G emissions are contributed by episodic emission sources as opposed to routine ones?

The average CH4 hourly emission rate predicted by the Ground Level Area Estimate for the two-day period spanning October 1st and 2nd, 2015 is 16.3 Mg/h (-2.3 / +2.6 Mg/h). Liquid unloadings comprise the vast majority of episodic emissions, with small contributions coming from blowdowns associated with maintenance and compressor engine starts and stops. The GLAE, which includes non-oil and gas sources, predicts that liquid unloadings contribute 5.3 Mg/h (-1.7 / +2.0 Mg/h), on average, over these two days. Therefore, episodic events contribute approximately 33% of the total emissions from the study area as estimated in the GLAE.

For the same period, natural gas sources contribute 12.0 Mg/h (-1.8 / +2.1 Mg/h) to the GLAE. Therefore, approximately 44% of CH4 emissions from the natural gas industry are due to episodic sources, on average, for the two-day period.

4.4 What is the distribution of facility-scale emissions from wellsites and gathering facilities? Does it exhibit a similar behavior to that observed in recent studies on wellsite components and other supply chain facilities, with a skewed, “fat tail” distribution of emissions?

Facility-scale emissions distributions of production well pads, as measured by both tracer and OTM33a, are discussed in section 2.5.1. Facility-scale measurements of gathering stations as measured by tracer are discussed in section 2.5.2.

4.5 How large are the uncertainties for both bottom-up and top-down estimates?"

The uncertainties for the Ground Level Area Estimate are listed in Table 3-1 and Table 3-2. The GLAE is a Monte Carlo model which augments measurements with estimates for all modeled sources (natural gas related sources and other sources). Every estimate is sampled from a representative distribution, which has some variability. On each iteration of the model a different combination of estimates from all source categories results in a different final GLAE. The model is run thousands of times to build an empirical distribution which represents an approximation of “all possible combinations” of input data. Therefore, GLAE estimates at the 2.5th and 97.5th fractiles of all Monte Carlo results represent a 95% confidence interval about the mean GLAE estimate, and are reported as the GLAE uncertainty.

The aircraft mass-balance emission estimate relative uncertainty is derived from the propagation of known measurement uncertainty and observed variability in the


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equation variables. It is therefore specific to each flight transect estimate (See Table 2-16 and Table 2-17). Additionally, for each mass-balance flight, we have two back-to-back downwind transects leading to two emission estimates per flight. We report the average of the two downwind transect emission estimates for each mass-balance flight.

The uncertainty associated with the average is lower by a factor of 1

√2 than the

uncertainty for each individual transect emission estimate.

Table 4-1:Summary of relative uncertainties for mass-balance equation variables derived from observations (from airplane and wind profiler) and resulting relative uncertainties for emission estimates from individual flight transect and the 2 transect average for each day.

Parameter October 1, 2015 October 2, 2015

Component of horizontal wind perpendicular to flight track

6 - 14% 17- 23%

CH4 downwind enhancement 18% 14%

PBL height 7 - 8% 17%

Individual transect estimate 28-31% 20-22%

2 transect average 15% 21%

The relative uncertainty on the aircraft total methane emission estimate on October 1, 2015 (15%) is lower than a previous aircraft study in the same region thanks to consistently uniform weather conditions in the study area (small uncertainty on wind component and PBL height) and estimates from two downwind transects.

We estimate a 6% 1-sigma uncertainty on the attribution for the Western portion of the study area. The relative uncertainty for the mass-balance estimate for the W portion total methane emissions is 13% and 20% on October 1 and 2, 2015 respectively. Combined, the relative uncertainty (1 sigma) for the top-down estimate of natural gas sources only emissions is 6+13=19% on October 1st and 6+20=26% on October 2nd.


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5 Study Conclusions

In this section we review the principal observations made in the study, focusing on the field campaign, measurement and modeling results. A principal objective of the study was a tested review of regional emission quantification methods. The design of the study supported comparisons between multiple measurement methods applicable at a range of device-, facility-, and regional-scales. The study also provided key insights into the crucial role of accurate and complete activity data to model regional-scale emissions.

5.1 Operational learnings

The field campaign design utilized for this study emphasized contemporaneous measurement of O&G facilities by multiple methods. This design was facilitated by having multiple, independent teams, involved in the field campaign during an intense five-week measurement period in September-October 2015. Measurements results were documented earlier in the report. In addition, several key qualitative and procedural observations can be made:

1) The onsite management of the ground facility level field campaign required two dedicated study team members (Tim Vaughn and Clay Bell, CSU) who consolidated daily measurement progress nightly and made tactical decisions for the next day’s measurements. These ‘campaign controllers’ were not part of any measurement team, allowing them to dispatch teams without the distraction of trying to also make measurements. Our study suggests that coordinated campaigns should not be attempted without independent controllers.

2) The selection of a dry-gas basin9 where study partners XTO Energy and Southwestern operated a dominant fraction of the basin production, allowed measurement teams to randomly select, and thus measure, a representative sample of facilities within the basin. The study partners provided data pre-campaign, hosted a pre-campaign visit to the study area, and provided extensive assistance during the campaign. During the campaign additional access and support by Kinder Morgan, CenterPoint Energy, and Enable Gas Transmission further extended the completeness and representativeness of the field campaign. The results achieved were greatly enabled by the unrestricted access and extensive operational data provided by these partners.

3) Activity data provided by the operators who hosted measurements (all five) provided an essential component to scale facility-scale measurements to study area scale. The addition, after the measurement campaign, of activity data from

9 The dry gas in the basin reduced the number of facility types, and the complexity of the equipment the study needed to measure.


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BHP Billiton produced the most complete set of activity data ever assembled for a regional study of O&G methane emissions. This activity data was also instrumental in eliminating possible causes of disagreements between measurement methods at the facility-scale or regional-scale.

4) The ability to vector multiple measurement methods to specific sub-regional clusters during the study increased the number of measurements possible. While this method was not developed for this study (it was also utilized by the study team in [12], [13]), results here (e.g. number >230 well pads and >30 compressor stations measured during the field campaign) illustrate that the efficiency of this sampling method.

5) As described in Section 2.3.3, pre-screening facilities to better characterize large emitters did not work given the wind conditions, topology and vegetation in the study area.

6) The wind profiler provided unique observations of the development of the boundary layer and horizontal wind direction and speed in the PBL on a real-time basis, and should be considered an integral part of any future basin-level emission study.

7) The aircraft-based mass-balance emission quantification presented here constrains early afternoon emissions. The meteorological conditions necessary to apply the mass balance approach require a well-mixed and close to fully developed PBL which typically happens late morning to midday in the summer and early afternoon in Spring and Fall.

8) Both the aircraft and bottom-up emission data show that regional midday methane emissions in the study area can vary from day to day.

9) Aircraft mass-balance emission estimation can be spatially explicit if strong steady winds are present over the study area. Strong steady (less variable) winds reduce the uncertainty in the estimated emissions but this may be partially offset by the smaller signal to noise of the methane enhancements measured by the aircraft downwind.

10) Raster flights and random drives can detect large emitters (large episodic releases or occasional malfunctions).

11) Further improvement in ethane in-situ measurements (stability, signal/noise) are needed to increase the sensitivity of the attribution method for methane


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emissions in dry gas fields and for marketed “dry” gas supply chain, due to the relatively low ethane to methane ratios as compared to ‘wet’ gas fields.

5.2 Emission measurement observations

This study assembled one of the most diverse set of quantitative top-down and bottom-up measurements made contemporaneously at O&G facilities. Comparisons between these methods provides significant insight into the strengths and weaknesses of various methods ([22], [18], [26]). In addition, measurements support previously-recognized characteristics:

Nearly all device- and facility-scale measurements exhibit skewed emission distributions where 5% of measured emitters are responsible for 30-60% of measured emissions.

High emissions at facilities are caused by a mix of high-emitting fugitives and short-term episodic emissions. Attribution of these emissions to either category is possible only with exhaustive contemporaneous activity data.

Several emission sources which were not measured in this study accounted for a significant fraction of total emissions as determined from engineering calculations or measurements and estimates from previous studies. In all cases, these sources were not measured due to resource limitations. These include:

o Manual and automatic unloadings at well sites, which were modeled utilizing data from previous studies [25] combined with activity estimates (time of day and duration) provided by study partners. Downwind tracer flux measurements helped validate that the selected emission factors were in the range of actual emissions at a few sites.

o Methane in combustion exhaust, which was modeled utilizing exhaust stack measurements made by the study partners within a year of the study. In this case, a large number of measurements was available (see [18]), made on the same class and model of engine (and often the same engine), operated by the same operators. Since these emissions account for a majority of gathering station compressor measurements, the strong correlation with tracer measurements validates that these measurements strongly represent these emissions.

Several other significant emission sources were not measured in the study, and while these sources did not account for as large a portion of modeled emissions, they represent potentially significant emission contributors and remain problematic measurement locations for future field campaigns.


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o Emissions from dehydrators at gathering compressor stations vary

dramatically in size. Due the location of the still column vents, these emissions (like combustion exhaust) are difficult to measure during a limited field campaign. However, in one case, emissions could be measured, providing an example for the emissions from these units. Commonly utilized models, such as GLY-CALC [27, p.], had sensitivity issues and did not align with the limited data set collected during the field campaign. Scale-up models utilized data measured in the field campaign. A more robust approach is recommended for future campaigns.

o Tank vent emissions could not be measured in all cases, due to safety concerns or lack of access to the tank vents. Tanks remain a problematic source location as many different primary emission sources present at the tank vents.

Paired measurements support the appropriateness of emission factors for several large source categories, including methane in compressor engine combustion exhaust [18], raw gas emission rates for liquid unloadings [22].

Properly briefed and trained, leak detection and repair (LDAR) teams can be highly effective in locating and measuring most emissions at well pad and compressor stations. In the case of well pads, device level screening using OGI and RMLD covered many more well pads than other methods.

The study completed a significant gathering line study (96 km) and detected only one pipeline leak. Data from this study suggest that auxiliary equipment on pipelines emits one to two orders of magnitude fewer emissions than leaks in the pipeline itself. However, data is limited and no definitive statements can be made. In total, gathering pipelines contribute only 1-2% of total study area emissions (400 kg/h versus 28 Mg/h).

The sole detected pipeline leak dominated emissions from gathering pipelines. This created wide uncertainty bounds for that emissions category. For other relatively new gathering systems similar leak frequencies are likely, and future campaigns should be planned to sample sufficient lengths of pipeline to characterize the emission rate per leak, and frequency of leaks. An estimate of the required field campaign attributes is provided in [28].

The study area had relatively little distribution pipeline length or customer meters, compared with other recent regional studies (e.g. [9]). In addition, total


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emission measured during the field campaign were in-line with recent studies [15], and represent a very small fraction of total estimated emissions for the study area – approximately 6 kg/h, or 0.02% of study area emissions.

5.3 Other study publications

In addition to the observations discussed here, results from this study are capture in multiple journal articles which have been referenced throughout the report. The articles can be divided into three rough logical groups.

First, measurement articles discuss the results of the measurement campaign segregated by the method utilized. In most cases, these papers include data from other measurement fields, or provide specific updates to the measurement methodology. These papers include tracer flux measurements (Yacovitch, et. al. [19]) and OTM33a measurements (Robertson, et. al. [20]), both of which pull in data from the DJ basin portions of this study or from other related studies. Aircraft measurements of facility emissions (“aircraft facility estimate”) is discussed in [26], with particular emphasis on the measurement methodology with closely-spaced sources.

Onsite measurement methods have been thoroughly documented by other studies, most notably [17] for compressor station measurements, and [15] for distribution systems and underground pipelines. Therefore, onsite measurements made in this study are presented in this report (Section 2.5), and in papers where the data was utilized. Gathering pipeline measurements are discussed in Zimmerle, et. al. [28], production measurements in Bell, et. al. [22], and gathering compressor station measurements in Vaughn, et. al. [18]. Distribution system measurements were utilized only in the GLAE development, and are discussed in Vaughn, et. al. [23].

Second, two papers provide pair-wise comparisons of facility-level emissions estimates (measurements) from multiple measurement methods. On-site, OTM33A and tracer flux estimates are compared for production facilities in Bell, et. al. [22]. On-site, tracer flux and aircraft estimates are compared for gathering compressor station measurements in Vaughn, et. al. [18].

Finally, estimates of study area emissions are discussed in a third group of papers. Schwietzke, et. al. [24] develops the aircraft mass balance estimate for October 1st and 2nd, 2015, which provided suitable atmospheric conditions for mass balance methods. Mielke-Maday, et. al. [29] utilizes raster flight data to split methane emissions estimated in the mass balance work between O&G operations and other sources. Comparisons of study area emissions using ground level models (GLAE) and aircraft models (AMB) are completed in Vaughn, et. al. [23]. This paper also includes additional discussion of the basin modeling methodology.


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While the scope of the study did not provide nationally-applicable estimates of activity or emission rates, a number of the papers do include observations which are applicable to scientific questions at the national-scale. Yacovitch, et. al. [19] and Robertson, et. al. [20] provide comparisons between emission rates from facilities in wet- and dry-gas basins. Gathering pipeline measurements made here are the first recent measurements of these pipeline systems. Zimmerle, et. al. [28] utilize observations from these measurements to estimate what fraction of a basin’s gathering pipeline network must be measured to bound total emissions from the gathering pipelines.


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6 Annexes

Annexes are included as separate computer files. This chapter lists the name of the files and provides a brief description of the contents.

Annex Number Title & Description

1 Screening and Guided Measurement Protocol

Facility screening plan to guide facility level measurements

2 Production Module: Measurement Protocol

Field measurement plan for production facility measurements

3 Gathering Station Module: Measurement Protocol

Field measurement plan for facility level measurements at Booster and Gathering stations

4 Onsite Detection and Measurement Protocol

Onsite measurement protocol for detecting emission sources utilizing optical gas imaging, and quantification of emission rates utilizing high flow sampling or other flow metering methods

5 Production Module: SOE Development

Modeling methods utilized to develop a facility-scale estimate of emissions for well pads.

6 Gathering Station Module: SOE Development

Modeling methods utilized to develop a facility-scale estimate of emissions for gathering compressor stations

7 Gathering Pipeline Study Area Model

Modeling methods utilized to develop a area-scale estimate of emissions for gathering pipelines

8 Distribution Study Area Model

Modeling methods utilized to develop a area-scale estimate of emissions for distribution systems

9 Non-O&G Study Area Model


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Modeling methods utilized to develop area-scale estimate of emissions from non-O&G (primarily agricultural) sources.

10 Gathering Pipeline Measurements

Design of the measurement campaign for gathering pipelines and a discussion of measurement methods.

11 Distribution System Measurements

Design of the distribution measurement campaign and a discussion of measurement methods.

12 Production Measurements

Onsite, OTM33a, and dual tracer release measurements made at production well pads.

13 Gathering Measurements

Onsite, dual tracer release, and aircraft spiral flight measurements made at gathering stations.

reconciling top-down and bottom-up methane emission estimates from onshore oil and gas ... ·...

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