Taxonomy, Life Cycle Assessment, and Meta-analyses for Improving Methane Emissions Estimates from Oil and Gas

Transcription

1 Taxonomy, Life Cycle Assessment, and Meta-analyses for Improving Methane Emissions Estimates from Oil and Gas Garvin Heath, PhD May 16, U.S. National Academy of Sciences Committee on Anthropogenic Methane Emissions in the United States: Improving Measurement, Monitoring, Reporting, and Development of Inventories

2 My Talk and the Committee s Charge Review relevant highlights of three papers related to most committee charges: Charge 4 Heath et al Harmonization of Initial Estimates of Shale Gas Life Cycle Greenhouse Gas Emissions for Electric Power Generation. PNAS. 111(31): E3167 E Charges 1, 2, 5, 6, 7 Heath et al Estimating U.S. Methane Emissions from the Natural Gas Supply Chain: Approaches, Uncertainties, Current Estimates, and Future Studies. Charges 4, 6, 7 Brandt A., G. Heath, and D. Cooley Methane Leaks from Natural Gas Systems Follow Extreme Distributions. ES&T 50 (22): JISEA Joint Institute for Strategic Energy Analysis 2

3 Bottom-up Engineering-based Methods for Environmental Assessment Inventory Cross-sectional - Temporal boundary: typically one year - Spatial boundary: global, national, sub-national - Sectoral boundaries Life Cycle Assessment (LCA) Sources: NREL and NOAA Longitudinal Sequence of processes, each modeled independently, summed across space and time, and scaled to a unit of final product attributable emissions Unit of end product (e.g., kwh) JISEA Joint Institute for Strategic Energy Analysis 3

4 Synthesis of Life Cycle GHG Emission Estimates for Electricity Generation IPCC SRREN SPM Fig. 8 1 Conventionally Produced NG JISEA Joint Institute for Strategic Energy Analysis 4

5 Charge 4: How to Present Results to Facilitate Comparisons: Results of Harmonization of Shale Gas LCAs 1 Methodological harmonization Aligns system boundaries Adjusts key parameters to align assumptions Achieves fair comparison of results from different studies Tends to consolidate results Source: Heath et al. (2014) JISEA Joint Institute for Strategic Energy Analysis 5

6 Natural Gas Life Cycle GHG Emissions: Comparing Unconventional to Conventional Gas and to Coal 1 Central Conclusions After methodological harmonization, unconventional gas when used to generate electricity is roughly equal to conventional gas in life cycle GHG emissions Comparing median estimates, both types of natural gas have half the emissions of coal. Source: Heath et al (Methane GWP = 30 for all categories) JISEA Joint Institute for Strategic Energy Analysis 6

7 Comparing and Interpreting Reported Methane Leakage Rates 1 Published Adjusted to Consistent Units Unconventional Gas Leakage Rate Unconventional Gas Leakage Rate Study Estimate (%) Reported units Howarth-Low 3.6 fugitive CH 4 / CH 4 produced Howarth-High 7.9 fugitive CH 4 / CH 4 produced Study Estimate (%) Consistent units Howarth-Low 2.8 CH 4 / NG produced Howarth-High 6.2 CH 4 / NG produced Jiang NR Jiang NR Skone 4.8 fugitive NG / produced gas Hultman 2.4 CH 4 / consumed gas Burnham 2 CH 4 / NG produced Stephenson 0.65 mixed Heath 1.3 CH 4 / NG produced Skone 3.9 CH 4 / NG produced Hultman 2.8 CH 4 / NG produced Burnham 2 CH 4 / NG produced Stephenson 0.66 CH 4 / NG produced Heath 1.3 CH 4 / NG produced Adjusted based on: Methane content of vented gas Loss rate from produced to consumed Volume to mass. JISEA Joint Institute for Strategic Energy Analysis 7

8 LCAs Rely on Data from Inventories, Which Are Evolving NG methane inventory for the year 2007 across six U.S. EPA GHG Inventories ( ) Sources: Larsen 2013 and U.S. EPA 2014 JISEA Joint Institute for Strategic Energy Analysis 8

9 Inventories Support Policy Development and Prioritization: Natural Gas Production Segment Methane Emissions Notes: The EPA s other category for emission reductions is applied proportionally to all categories to calculate net emissions. Assumes 100-yr GWP of methane = 25 GWP reflects EPA s GHG Reporting Program as well as its recently published 2015 U.S. GHGI. Source: U.S. EPA 2014 GHG Inventory JISEA Joint Institute for Strategic Energy Analysis 9

10 Charges 1, 2, 5, 6, and 7: Inventory Improvement Opportunities 2 Goals: Summarize methods and results of the U.S. GHGI Identify potential gaps and barriers to improvement Identify opportunities to improve accuracy Foci: Methane emissions from the natural gas sector National GHG inventory Implications for other inventories (e.g., state) and other pollutants. JISEA Joint Institute for Strategic Energy Analysis 10

11 The U.S. GHGI: A Critical Resource 2 The U.S. Greenhouse Gas Inventory (GHGI) identifies and quantifies emission sources and sinks of greenhouse gases (GHG) from human activities in the United States. The U.S. Environmental Protection Agency (EPA) publishes the U.S. GHGI. Many agencies, organizations, and researchers rely on its results for analyses and decision making. The U.S. GHGI is a critical resource for: Understanding the U.S. contribution to global climate change Tracking trends in GHG emission sources and sinks Identifying and prioritizing abatement opportunities within the United States Informing policy and investment decision making. JISEA Joint Institute for Strategic Energy Analysis 11

12 The U.S. GHGI: A Critical Resource 2 The U.S. Greenhouse Gas Inventory (GHGI) identifies and quantifies emission sources and sinks of greenhouse gases (GHG) from human activities in the United States. The U.S. Environmental Protection Agency (EPA) publishes the U.S. GHGI. Many agencies, organizations, and researchers rely on its results for analyses and decision making. The U.S. GHGI is a critical resource for: Understanding the U.S. contribution to global climate change Tracking trends in GHG emission sources and sinks Identifying and prioritizing abatement opportunities within the United States Informing policy and investment decision making. JISEA Joint Institute for Strategic Energy Analysis 12

13 Top-down (TD) and Bottom-up (BU) Studies 2 Nomenclature not consolidated on definition of top-down or bottom-up: Top-down: Infers emissions from measurements of atmospheric methane concentrations or atmospheric models. Bottom-up: Focuses on the specific source or activity causing the emissions. Measurement-based estimate or modeled (e.g., inventory see bottom panel). Figure: NREL and NOAA, 2014 Definitions: White House Climate Action Plan. JISEA Joint Institute for Strategic Energy Analysis 13

14 Top-down and Bottom-up Studies: Roles to Improve Inventory 2 Both top-down (TD) and bottom-up (BU) studies have uncertainty and potential for inaccuracy neither is truth Both have roles to improve inventory. For example: TD: Useful as comparison to inventory estimates; any differences could help generate hypotheses BU: Measurement studies can update outdated emission factors (EFs). POTENTIAL IMPROVEMENTS Improve capability of TD measurements to verify inventory, for example: Improve source attribution to better align TD and BU study system boundaries Improve understanding of non-o&g methane sources, especially ones that confound current attribution methods Improve ability to align measurements across scales (e.g., from regional to facility). JISEA Joint Institute for Strategic Energy Analysis 14

15 Inventory Improvement Through BU Measurement Studies 2 Challenges with currently used EFs: Not representative Outdated Sampling bias Sample size Mean emission factors (EFs) capture fat tail? All salient dimensions of emission variability captured? POTENTIAL IMPROVEMENTS Update EFs for prioritized emission sources categories Focus effort of new studies on ensuring robust sample size, strong sampling design to capture source variability, and minimization of self-selection bias Leverage available evidence to explore how to characterize emission variability within the EF metric Explore regional variability and variability along other dimensions. JISEA Joint Institute for Strategic Energy Analysis 15

16 Inventory Improvement for Activity Factors 2 Most efforts to improve the inventory have focused on EFs activity factors (counts) also need attention: Data sources GHGRP or new ones Methods: transparency, simplicity, and accuracy Balance the need for consistent time series with the need to improve current accuracy. POTENTIAL IMPROVEMENTS Develop new data sources to improve accuracy, completeness, and methodological simplicity Develop methods for quantification of activity factor uncertainty. JISEA Joint Institute for Strategic Energy Analysis 16

17 Inventory Improvement: Completeness and Structure 2 Prioritized gaps in current knowledge, for example: Abandoned wells Measurements on gathering pipelines After the meter leaks at site of end use Well work-overs that are not recompletions* Inventory structure Currently organized sectorally, which creates challenges when comparing to a measurement representative of a certain spatial domain Oil and gas wells in the same area Associated gas Certain segments are grouped (e.g., gathering with production). POTENTIAL IMPROVEMENTS Fill prioritized source gaps in GHGI Gridded inventory to enhance measurementbased validation. Align future studies to the structure of the GHGI for easier incorporation Or Consider restructuring the inventory to better capture robust results of recent studies *Work-overs are included in the GHGI, but are defined as recompletions. Other work-over activities can also be performed in the industry. JISEA Joint Institute for Strategic Energy Analysis 17

18 Uncertainty Quantification 2 Quantification of uncertainty is critical for informed decision making, communication, and verification with measurements. Currently, the GHGI: Uses Monte Carlo parametric uncertainty quantification, with lognormal distributions assumed in almost all cases Reports an uncertainty range that has not changed since 2010 Uses expert judgment to assign uncertainty for activity factors. POTENTIAL IMPROVEMENTS Ensure sponsored studies robustly quantify uncertainty Strengthen uncertainty quantification methods and efforts JISEA Joint Institute for Strategic Energy Analysis 18

19 New Research Efforts in the Context of a Large Number of Studies (as of 2015) 2 POTENTIAL IMPROVEMENTS Enhance coordination among studies Increase confidence in inventory accuracy by pairing measurements with inventory contemporaneously and systematically. Source: Heath et al JISEA Joint Institute for Strategic Energy Analysis 19

20 Evidence for Methane Emission Fat Tails and Implications for Science, Policy and Mitigation 3 Methane Leaks from Natural Gas Systems Follow Extreme Distributions. Environmental Science & Technology. 50 (22), Adam Brandt, 1 Garvin Heath, 2 and Dan Cooley 3 1 Department of Energy Resources Engineering, Stanford University 2 National Renewable Energy Laboratory 3 Department of Statistics, Colorado State University JISEA Joint Institute for Strategic Energy Analysis 20

21 Several Studies Measured Emissions from Components Named the Same 3 We (Cooley) tested whether the samples from two different studies that were named the same come from the same underlying population Kolmogorov- Smirnov (K-S) test JISEA Joint Institute for Strategic Energy Analysis 21

22 Charges 4, 6, 7: How to Ensure Comparability and Appropriate Use for Inventories, and Recommended Research 3 Meta-analysis Separate studies of a given device type fail K-S tests, suggesting their samples are not drawn from the same population. Meta-analysis is not advised. However, future studies could be designed to match prior ones and take advantage of the increased power of meta-analysis (though practical challenges are acknowledged) Future studies, and especially meta-analyses, would be greatly enhanced with a common taxonomy of equipment, components, and activities Other implications of the results of this study Available data could be leveraged to develop empirically-based confidence intervals for source categories to improve accuracy of inventory uncertainty quantification. Researchers and R&D funding organizations can use these results to improve sampling design of new studies to enhance the robustness of results, minimize sample sizes, and address critical gaps in knowledge Much remains unknown about distributions of emissions from O&G sources: Temporal persistence Root causes of super-emitter behavior Source-specific super-emitter properties. JISEA Joint Institute for Strategic Energy Analysis 22

23 Discussion Further Information Garvin Heath Acknowledgements U.S. DOE Novim JISEA JISEA Joint Institute for Strategic Energy Analysis 23

24 Additional Citation IPCC SRREN: Sathaye, J., O. Lucon, A. Rahman, J. Christensen, F. Denton, J. Fujino, G. Heath, S. Kadner, M. Mirza, H. Rudnick, A. Schlaepfer, A. Shmakin, 2011: Renewable Energy in the Context of Sustainable Development. In IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation [O. Edenhofer, R. Pichs Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY. For results of the LCA Harmonization Study: JISEA Joint Institute for Strategic Energy Analysis 24

25 Supplemental Slides JISEA Joint Institute for Strategic Energy Analysis 25

26 U.S. GHGI Challenges JISEA Joint Institute for Strategic Energy Analysis 26

27 Challenges to Constructing the U.S. Inventory Missing emission sources Some sources of emissions are not quantified in the inventory. Undercounting of equipment or activities Other sources indicate higher activity counts then the inventory. Low sample sizes* Bottom-up measurements are expensive and are logistically challenging, leading to sample sizes lower than ideal. EF equipment variability (i.e., super-emitters ) is not captured An EF-based on an average does not capture the impact of a few very high superemitters. Sampling bias in construction EFs The sample may be based on self-selection and the age of the equipment measured might not match current conditions. Differences between U.S. regions EFs may not adequately capture variability between regions. Source: ICF 2014 and Brandt 2014 *A challenge more so for other inventories than the U.S. GHGI JISEA Joint Institute for Strategic Energy Analysis 27

28 Limitations of the Structure of the U.S. GHGI Categorization of emissions by industry sector can create challenges for: Sectoral allocation of measured emissions from sites with coproducts (e.g., NG, oil and condensate) Comparison of measurement studies to the inventory Categorization of gathering within production segment creates challenges in tracking emissions that are separated in space and by ownership and activity JISEA Joint Institute for Strategic Energy Analysis 28

29 Limitations of the Scope of the U.S. GHGI U.S. GHGI has some known gaps, including: Abandoned wells or other derelict infrastructure Emissions from produced water management, especially retention ponds Management of solid waste from natural gas operations, including land application Well work-overs that are not recompletions* Well testing in production CH 4 emission classified elsewhere in the EPA inventory: Associated gas from wells classified as oil wells Behind the meter yet prior to end use combustion emissions * Work-overs are included in the GHGI but are defined as recompletions. Other work-over activities can also be performed in the industry. JISEA Joint Institute for Strategic Energy Analysis 29

30 Emission Variability in the U.S. GHGI Construction of average EFs when there are a small number high emitters is problematic, exacerbated by low sample size that fail to capture the high emitters in a sample. Bottom-up studies* suggest that unintentional leakage rates vary greatly among devices. o Most devices do not leak. o A small fraction of devices leak excess gas (small %). o A very small fraction (<<1%) leak a large amount. These sources often contribute a large fraction of the total leakage. Alvarez et al. (2012) Well pad emissions Harrison et al. (2012) NGML et al. (2006) Gas plants, other An example: Fifty (50) of 75,000 source points (0.06%) resulted in 60% of all emissions. * See Table S6 in Brandt 2014 SM for tabular evidence of super-emitters," and Figure S2 JISEA Joint Institute for Strategic Energy Analysis 30

31 Emission Variability in the U.S. GHGI Emissions, activities, use of control technologies, and abatement potential may vary by region. Some major sources of variation that could be important include: State regulation Geology, such as the water-to-gas ratio or the gas-to-oil ratio Age of infrastructure Concentration of operators (i.e., how many) Market capitalization of operators Differences in and number of jurisdictions (e.g., Uinta) Gas composition Type of gas, such as shale, tight, conventional, coal bed methane Regional price of gas Market structure for each supply chain segment Climate JISEA Joint Institute for Strategic Energy Analysis 31

32 Uncertainty Analysis in the U.S. GHGI The parametric uncertainty analysis consists of Monte Carlo simulations. Examines 12 major emission source categories accounting for 92% of total natural gas methane emissions Distributions based on data sources (e.g., EPA/GRI [1996]) and expert judgment Baseline methane emissions: 6.2 MMt methane 95% confidence interval: 5.0 (- 19%) to 8.0 (+ 30%) MMt methane Another source of uncertainty is methodological. The U.S. GHGI has changed methods over time. Uncertainty bands in the inventory have not changed since 2010, but EPA has plans to update them. JISEA Joint Institute for Strategic Energy Analysis 32

33 Obtaining an Adequate Sample Size Representiveness of the sample can be negatively effected by a low sample size EPA/GRI study made significant sampling efforts (e.g., >200,000 sources) that have not been surpassed Used stratification to mitigate variability due to smaller-than-ideal sample sizes Stratification: Reduces the number of samples required to reach a given level of uncertainty Stratification requires collection of accurate activity counts by stratum. Otherwise accuracy will not improve Source: Brandt 2014 JISEA Joint Institute for Strategic Energy Analysis 33

34 GHG Reporting Program (GHG-RP) In 2009, the EPA published a rule requiring reporting of GHGs from sources emitting >25 Gg CO 2 e/yr. This facility level reporting is applicable to: Direct greenhouse gas emitters Fossil fuel suppliers Industrial gas suppliers Facilities that inject CO 2 underground. Not a measurement or inventory study due to the emission threshold for reporting requirements Used to provide a better understanding of GHG emission sources and to guide emission reduction policies and programs Source: Gg = gigagrams or thousand metric tonnes JISEA Joint Institute for Strategic Energy Analysis 34

35 Methane Measurement Studies and Inventories JISEA Joint Institute for Strategic Energy Analysis 35

36 Many New Measurement Studies JISEA Joint Institute for Strategic Energy Analysis 36

37 CH 4 Emission TD Measurement Studies through Feb ID# Pub. Date Location Units Measurement Platform Days Area (km 2 ) Top-down Studies: Ground Tower Plane Satellite 1 Peischl 2015 Haynesville Fayetteville Marcellus Samples - - ~250-5 ~200k 2 Cambaliza 2015 Indianapolis Samples ~3.5k 3 Brantley 2014 Barnett Denver-Julesburg Eagle Ford Green River Samples Schneising 2014 Bakken Eagle Ford Samples NR Caulton 2014a Bakken Samples ~520k 6 Caulton 2014b North Appalachian Samples - - ~2k k 7 Pétron 2014 Denver-Julesberg Samples ~6k 8 Miller 2013 TX, OK, LA U.S. Samples - ~900k NR ~12k - 9 Karion 2013 Uintah Samples - - ~ Pétron 2012 Denver-Julesburg Samples ~100 ~ Kort 2008 U.S. & Canada Samples - - ~ Katzenstein 2003 TX, OK, KS Samples ~ ~590k 13 Hsu 2010 SoCAB Samples NR Wunch 2009 SoCAB Samples NR Peischl 2013 SoCAB Flights ~28k 16 Wennberg 2012 SoCAB Flights ~28k 17 Xiao 2008 U.S. N/A NR Wang 2004 U.S. N/A NR - - ~830K - JISEA Joint Institute for Strategic Energy Analysis 37

38 CH 4 Emission BU Measurement Studies through Feb ID# Pub. Date Location Units Supply Chain Total Component Total Sites Production Processing Gath. & Trans. & Boost Storage Dist. 19 Subramanian 2015 U.S. Components ~1.4k - ~1.4k 40 AR, AL, CO, KS, LA, 20 Mitchell 2015 NM, NY, OK, PA, TX, Sites UT, WV, WY 2 Cambaliza 2015 Indianapolis Samples ~3.5k 21 McKain 2015 Boston Leaks NR NR CAPP 2014 Canada Components ~280k ~280k OIPA OK Components Allen 2014a RM, MC, GC, AP Components Allen 2014b RM, MC, GC, AP Components Kang 2014 PA Components Allen 2013 RM, MC, GC, AP Components ~ ~ Alvarez 2012 Fort Worth, TX Components NR NR Harrison 2011 U.S. Components GTI 2009 U.S. Components ~3k ~3k Clearstone 2002 U.S. Components - ~100k ~100k 4 32 Johnson 2014 Barnett Sites NGML 2006 Canada Sites ~75k Chambers 2006 Alberta Canada Sites ConocoPhillips 2006 Canada Sites Shorter 1997 TX, OK, CA Sites 5 8 NR EPA/GRI 1996 U.S. Sites NR Carbon Limits 2014 U.S. & Canada Sites ~2k ~600 ~2k - ~58k ~4.6k 39 Rich 2014 Barnett Components NR Jackson 2014 Washington, D.C. Leaks ~5.9k ~5.9k Phillips 2013 Boston Leaks ~3.4k ~3.4k - JISEA Joint Institute for Strategic Energy Analysis 38

39 Emission Inventories Published through May 2014 JISEA Joint Institute for Strategic Energy Analysis 39

40 Emission Inventories through May 2014 ID# Pub. Date Years Location Area GHG (km 2 ) Emissions Other Emissions Prod. Gath.& Trans.& Proc. Dist. Boost. Stor. 1 U.S. EPA U.S. 9800k Yes - YES YES YES YES YES 2 WRAP 2005a+b 2002, states 5200k - NO x, SO 2, VOC YES YES NO NO NO 3 Jeong CA On and Offshore ~420k Yes - YES YES YES YES YES 4 CARB CA On and Offshore ~420k Yes - YES YES YES YES NO 5 WRAP 2013a+b 2009, 2015 Williston <480k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 6 WRAP 2012d 2009 WY ~79k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 7 WRAP c 2006, 2015 Powder River ~42k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 8 WRAP 2010a+b 2006, 2012 Wind River ~13k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 9 BIA Wind River ~13k - CO, NO x, PM 2.5, PM 10, SO x, TOG, VOC YES YES NO NO NO 10 WRAP 2012a+b 2006, 2015 Greater Green River ~24k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 11 Reid Greater Green River ~28k - CO, HONO, NO x, PM, SO 2, TOG, YES NR NO NO NO 12 UT BLM , 2021 Uintah ~23k - CO, NH 3, NO x, PM 2.5, PM 10, SO 2, TOG YES YES NO NO NO 13 WRAP 2009e+f 2006, 2012 Uintah ~23k - CO, NO x, PM, SO x, VOC YES NR NO NO NO 14 WRAP 2009a+b 2006, 2012 Piceance ~11k - CO, NO x, PM, SO x, VOC YES NR NO NO NO 15 WRAP 2008a+b 2006, 2010 Denver-Julesburg ~110k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 16 WRAP 2009g 2006, 2012 North San Juan ~3k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 17 WRAP 2009c+d 2006, 2012 South San Juan ~9k - CO, NO x, PM, SO x, VOC YES YES NO NO NO 18 NMED South San Juan ~9k - NO x, SO 2, VOC YES NR NO NO NO 19 TCEQ TX - On and Offshore >700k - CO, NO x, PM 2.5, PM 10, SO 2, VOC, YES YES NO NO NO 20 TCEQ 2009a 2002, 2005, TX Onshore ~700k - CO, HAPs, NO x, PM 2.5, PM 10, SO 2, VOC YES NO NO NO NO 21 TCEQ , 1993, 1996, TX - Onshore ~700k - CO, NO x, PM 2.5, PM 10, SO 2, TOG, VOC YES NR NO NO NO 22 Zavala-Araiza Barnett Shale ~31k - VOC YES NR NO NO NO 23 Heath Barnett Shale ~8k Yes - YES YES YES YES YES 24 EDF , 2009 Barnett Shale ~8k Yes HAP, NO x, VOC YES YES YES YES NO 25 TCEQ 2009c 2009 Barnett Shale ~8k - CO, NO x, VOC YES NR NO NO NO 26 TCEQ , 2012, 2015, 2018 Eagle Ford Shale ~20k - CO, NO x, VOC YES YES NO NO NO 27 ADEQ 2011a + b 2008 Fayetteville Shale ~15k Yes CO, NO x, PM 10, SO 2, VOC YES YES YES YES NO 28 TCEQ 2009b 2009, Haynesville Shale ~14k Yes CO, NO x, VOC YES YES NO NO NO 29 U.S. DOI /2001 Breton Area NR - NO x, SO 2 YES NO NO NO NO 30 PA DEP , 2012 PA ~46k Yes CO, NO x, PM 2.5, PM 10, SO x, TOG, VOC YES NR NO NO NO 31 PA DEP , 1999 PA ~46k Yes - YES YES YES YES YES JISEA Joint Institute for Strategic Energy Analysis 40