Publications

Methane (CH4) leakage from natural gas (NG) pipelines poses an environmental, safety, and economic threat to the public. While previous leak detection and quantification studies focus on the aboveground infrastructure, the analysis of underground NG pipeline leak scenarios is scarce. Furthermore, no data from controlled release experiments have been published on the accuracy of methods used to (1) quantify emissions from an area source and (2) use these emissions to quantify the size of a subsurface leak. This proof-of-concept work uses CH4 mole fraction, as measured by a single gas sensor, as an input to a simple dispersion-based model (WindTrax) under ideal conditions (i.e., in a field) and compares the calculated emissions to the known controlled NG release rates. The aboveground and surface CH4 mole fractions were measured for 5 days at a field testbed using controlled underground release rates ranging from 0.08 to 0.52 kg hr–1 (3.83–24.94 ft3 hr–1). Results confirmed that the mean normalized CH4 mole fraction increases as the atmosphere transitions from the Pasquill–Gifford (PG) stability class A (extremely unstable) to G (extremely stable). The estimated surface CH4 emissions showed large temporal variability, and for the emission rates tested, at least 6 h of data are needed to have a representative estimate from subsurface pipeline leaks (±27% of the controlled release rate on average). The probability that the emission estimate is within ±50% of the controlled release rate (P±50%) is approximately 50% when 1 h of data is collected; the probability approaches 100% with 3–4 h of data. Findings demonstrate the importance of providing enough data over time for accurate estimation of belowground leak scenarios. By adopting the estimation method described in this study, operators can better estimate leakage rates and identify and repair the largest leaks, thereby optimizing annual greenhouse gas emissions reductions and improving public safety.

Continuous Monitoring (CM) solutions have been identified as one method to address methane emissions at oil and gas (O&G) facilities, as these solutions may facilitate faster emission detection and repair than traditional survey methods. This study tested 13 CM solutions over 12 weeks using single-blind controlled testing. Controlled release rates ranged from 0.08 to 6.75 kg CH4/hr and lasted 18 minutes to 8 hours. Six solutions demonstrated 90% detection limits (DLs) within the range of controlled releases, 0.5 [0.3, 0.6] kg CH4/hr to 6.8 [5.9, 8.0] kg CH4/hr. Of the six solutions, 4 had False Positive (FP) rates of 7.8-14%, and four had False Negative rates (FN) of 8-34.1%. Compared with Ilonze et al., these results show retested solutions balancing method sensitivity and FP and FN rates. All scanning/imaging solutions had high localization (≥ 40%) precision and accuracy to the equipment unit. Eleven of 13 solutions were tested for quantification; three had a mean relative quantification error ranging from 33.4 [0.85, 66.0] % to 96.1 [63.4, 128.9] %, 95% CI for leaks between 0.1 – 1 kg CH4/hr. For larger emissions (> 1 kg CH4/hr), nine solutions had a mean relative quantification error ranging from 2.8 [-20.3, 25.9] % to 32.8 [17.2, 48.5] %, 95% CI. Relative to previous studies, uncertainties in quantification estimates decreased, as did FN and FP rates, with improved detection limits for 2 of the four retested solutions. These findings highlight that continuous, rigorous testing enhances solution performance, with notable improvements observed across multiple test programs using the same test protocol.

One of the biggest risks to safety on offshore platform safety is the ignition of high-pressure natural gas streams. Currently, the size and number of fugitive emissions on offshore platforms is unknown and methods used to detect fugitives have significant shortcomings. To investigate the frequency, size, and potential impact of fugitives, a data collection exercise was conducted using incidents reported, leak survey data, and independent measurements. The size and number of fugitives on offshore facilities were simulated to investigate likely areas of safety concern. Incident reports indicate in 2021 there were 113 reports of gas leaks on 1119 offshore facilities, suggesting 0.02 fugitives per Type 1 facility (older, shallow-water platforms) and 0.31 fugitives per Type 2 facility (larger deeper-water facilities). Leak survey data report 12 fugitives per Type 1 facility (average emission 0.6 kg CH4 h−1 leak−1) and 15 fugitives per Type 2 facility (average emission 1.5 kg CH4 h−1 leak−1). Reconciliation of direct measurements with a bottom-up model suggests that the number of fugitive emissions generated from the leak report data is an underestimate for Type 1 platforms (44 fugitives facility−1; average emission 0.6 kg CH4 h−1 leak−1) and in general agreement for the Type 2 platforms (15 fugitives facility−1; average emission 1.5 kg CH4 h−1 leak−1). Analysis of the fugitive emission rates on an offshore platform suggests that gas will not collect to explosive concentration if any air movement is present (>0.36 mph); however, large volumes of air (~600 m3) near representative leaks on the working deck could become explosive in hour-long zero-wind conditions. We suggest that wearable technology could be employed to indicate gas build up, safety regulations amended to consider low-wind conditions and real-world experiments are conducted to test assumptions of air mixing on the working deck.

Continuous emission monitoring (CM) solutions promise to detect large fugitive methane emissions in natural gas infrastructure sooner than traditional leak surveys, and quantification by CM solutions has been proposed as the foundation of measurement-based inventories. This study performed single-blind testing at a controlled release facility (release from 0.4 to 6400 g CH4/h) replicating conditions that were challenging, but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicated a 90% probability of detection (POD) of 3–30 kg CH4/h; 6 of 11 solutions achieved a POD < 6 kg CH4/h, although uncertainty was high. Four had true positive rates > 50%. False positive rates ranged from 0 to 79%. Six solutions estimated emission rates. For a release rate of 0.1–1 kg/h, the solutions' mean relative errors ranged from −44% to +586% with single estimates between −97% and +2077%, and 4 solutions' upper uncertainty exceeding +900%. Above 1 kg/h, mean relative error was −40% to +93%, with two solutions within ±20%, and single-estimate relative errors were from −82% to +448%. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicates that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting.

Aerial surveys, while effective in detecting emissions from upset conditions, face challenges in fully capturing CH4 emissions due to their temporal limitations, variability in measurements, and detection thresholds. Conversely, annual inventories submitted by operators likely don’t include emissions from failure events. This study introduces a novel methodology that utilizes the Mechanistic Air Emissions Simulator (MAES) to integrate two highly variable estimation methods: inventory and aerial methods. The proposed methodology identifies and characterizes failure events with site-specific information, thereby enhancing the accuracy of inventory programs through the so-called measurement-informed inventories (MIIs). Furthermore, it emphasizes the importance of carefully comparing instantaneous emission measurements from aerial surveys with annual average emissions reported in inventories, as they have distinct timeframes. Colorado State University (CSU) collaborated with the Colorado Department of Public Health and Environment (CDPHE) to utilize this approach to enhance reported emissions from the upstream sector in Colorado Denver-Julesburg (DJ) basin. This initiative is part of the state’s efforts to reduce emissions under the Upstream greenhouse gas (GHG) Intensity Program. The goal was to incorporate measured emissions from failure events conducted by Carbon Mapper (CM) in the simulations to derive a multiplier that rectifies for potential omissions of emissions from abnormal conditions within the oil and gas (O&amp;G) sector. To simplify the simulation process, prototypical sites were defined in conjunction with operators and are used to represent groups of O&amp;G facilities in the basin with similar configuration. The outcomes of this work indicate that inventories are likely underestimating total emissions, as an additional 16.4% of total emissions from abnormal events is estimated for the basin.

Methane is a greenhouse gas and identified as a key driver of near-term climate change. Bottom-up approaches estimate annual methane loss from US natural gas production and transport at 6 Tg, but recent studies suggest this may be an underestimate. To investigate this possibility, an equipment-based emissions inventory, using EPA emission factors, was developed to calculate methane emissions from oil and gas operations in the Delaware Basin, USA. Emission factors and activity data were then updated using contemporary and region-specific measurement data. The original inventory estimated emissions at 315 Gg CH4 y-1 (gas production-normalized rate of 0.6% loss), while the updated inventory estimated emissions of 1,500 Gg CH4 y-1 (2.8% loss). The largest changes resulted from large fugitive emissions from oil production (+430 Gg CH4 y-1), updating maintenance activity emissions (+214 Gg CH4 y-1), considering flaring inefficiency (+174 Gg CH4 y-1), and the inclusion of associated gas venting (+136 Gg CH4 y-1). This study suggests that a systematic underestimate probably exists in current bottom-up inventories and identifies sources currently missing or may be incorrect. We also strongly recommend that emission factors should be validated through direct comparison against measurement campaigns that include long-tail distributions typical of oil and gas activities.

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionORIGINAL ARTICLEThis notice is a correctionCorrection to Measurements of Methane Emissions from Natural Gas Gathering Facilities and Processing Plants: Measurement ResultsAustin L. Mitchell, Daniel S. Tkacik, Joseph R. Roscioli, Scott C. Herndon, Tara I. Yacovitch, David M. Martinez, Timothy L. Vaughn, Laurie Williams, Melissa Sullivan, Cody Floerchinger, Mark Omara, R. Subramanian, Dan Zimmerle, Anthony J. Marchese, and Allen L. Robinson*Cite this: Environ. Sci. Technol. 2015, 49, 20, 12602Publication Date (Web):October 7, 2015Publication History Published online7 October 2015Published inissue 20 October 2015https://doi.org/10.1021/acs.est.5b04018Copyright © 2015 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views734Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (191 KB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information Get e-Alerts

Abstract. The accurate reporting of methane (CH4) emissions from point sources, such as fugitive leaks from oil and gas infrastructure, is important for evaluating climate change impacts, assessing CH4 fees for regulatory programs, and validating methane intensity in differentiated gas programs. Currently, there are disagreements between emissions reported by different quantification techniques for the same sources. It has been suggested that downwind CH4 quantification methods using CH4 measurements on the fence-line of production facilities could be used to generate emission estimates from oil and gas operations at the site level, but it is currently unclear how accurate the quantified emissions are. To investigate model accuracy, this study uses fence-line simulated data collected during controlled release experiments as input for eddy covariance, aerodynamic flux gradient and the Gaussian plume inverse methods in a range of atmospheric conditions. The results show that both the eddy covariance and aerodynamic flux gradient methods underestimated emissions in all experiments. Although calculated emissions had significant uncertainty, the Gaussian plume inversion method performed better. The uncertainty was found to have no significant correlation with most measurement variables (i.e. downwind measurement distance, wind speed, atmospheric stability, or emission height), which indicates that the Gaussian method can randomly either underestimate or overestimate emissions. For eddy covariance, downwind measurement distance and percent error had negative correlation indicating that far away emissions sources were likely underestimated or be undetected. The study concludes that using fence-line measurement data as input to eddy covariance, aerodynamic flux gradient or Gaussian plume inverse method to quantify CH4 emissions from an oil and gas production site is unlikely to generate representative emission estimates.