Point Sensor Networks Struggle to Detect and Quantify Short Controlled Releases at Oil and Gas Sites.

This study evaluated multiple commercially available continuous monitoring (CM) point sensor network (PSN) solutions under single-blind controlled release testing conducted at operational upstream and midstream oil and natural gas (O&G) sites. During releases, PSNs reported site-level emission rate estimates of 0 kg/h between 38 and 86% of the time. When non-zero site-level emission rate estimates were provided, no linear correlation between the release rate and the reported emission rate estimate was observed. The average, aggregated across all PSN solutions during releases, shows 5% of the mixing ratio readings at downwind sensors were greater than the site's baseline plus two standard deviations. Four of seven total PSN solutions tested during this field campaign provided site-level emission rate estimates with the site average relative error ranging from -100% to 24% for solution D, -100% to -43% for solution E, -25% for solution F (solution F was only at one site), and -99% to 430% for solution G, with an overall average of -29% across all sites and solutions. Of all the individual site-level emission rate estimates, only 11% were within ±2.5 kg/h of the study team's best estimate of site-level emissions at the time of the releases.

Performance of Continuous Emission Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

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.

Temporal variability largely explains top-down/bottom-up difference in methane emission estimates from a natural gas production region.

This study spatially and temporally aligns top-down and bottom-up methane emission estimates for a natural gas production basin, using multiscale emission measurements and detailed activity data reporting. We show that episodic venting from manual liquid unloadings, which occur at a small fraction of natural gas well pads, drives a factor-of-two temporal variation in the basin-scale emission rate of a US dry shale gas play. The midafternoon peak emission rate aligns with the sampling time of all regional aircraft emission studies, which target well-mixed boundary layer conditions present in the afternoon. A mechanistic understanding of emission estimates derived from various methods is critical for unbiased emission verification and effective greenhouse gas emission mitigation. Our results demonstrate that direct comparison of emission estimates from methods covering widely different timescales can be misleading.

Detection Limits of Optical Gas Imaging for Natural Gas Leak Detection in Realistic Controlled Conditions.

Optical gas imaging (OGI) is a commonly utilized leak detection method in the upstream and midstream sectors of the U.S. natural gas industry. This study characterized the detection efficacy of OGI surveyors, using their own cameras and protocols, with controlled releases in an 8-acre outdoor facility that closely resembles upstream natural gas field operations. Professional surveyors from 16 oil and gas companies and 8 regulatory agencies participated, completing 488 tests over a 10 month period. Detection rates were significantly lower than prior studies focused on camera performance. The leak size required to achieve a 90% probability-of-detection in this study is an order-of-magnitude larger than prior studies. Study results indicate that OGI survey experience significantly impacts leak detection rate: Surveyors from operators/contractors who had surveyed more than 551 sites prior to testing detected 1.7 (1.5-1.8) times more leaks than surveyors who had completed fewer surveys. Highly experienced surveyors adjust their survey speed, examine components from multiple viewpoints, and make other adjustments that improve their leak detection rate, indicating that modifications of survey protocols and targeted training could improve leak detection rates overall.

"Good versus Good Enough?" Empirical Tests of Methane Leak Detection Sensitivity of a Commercial Infrared Camera.

Methane, a key component of natural gas, is a potent greenhouse gas. A key feature of recent methane mitigation policies is the use of periodic leak detection surveys, typically done with optical gas imaging (OGI) technologies. The most common OGI technology is an infrared camera. In this work, we experimentally develop detection probability curves for OGI-based methane leak detection under different environmental and imaging conditions. Controlled single blind leak detection tests show that the median detection limit (50% detection likelihood) for FLIR-camera based OGI technology is about 20 g CH4/h at an imaging distance of 6 m, an order of magnitude higher than previously reported estimates of 1.4 g CH4/h. Furthermore, we show that median and 90% detection likelihood limit follows a power-law relationship with imaging distance. Finally, we demonstrate that real-world marginal effectiveness of methane mitigation through periodic surveys approaches zero as leak detection sensitivity improves. For example, a median detection limit of 100 g CH4/h is sufficient to detect the maximum amount of leakage that is possible through periodic surveys. Policy makers should take note of these limits while designing equivalence metrics for next-generation leak detection technologies that can trade sensitivity for cost without affecting mitigation priorities.

Variation in Methane Emission Rates from Well Pads in Four Oil and Gas Basins with Contrasting Production Volumes and Compositions.

Atmospheric methane emissions from active natural gas production sites in normal operation were quantified using an inverse Gaussian method (EPA's OTM 33a) in four major U.S. basins/plays: Upper Green River (UGR, Wyoming), Denver-Julesburg (DJ, Colorado), Uintah (Utah), and Fayetteville (FV, Arkansas). In DJ, Uintah, and FV, 72-83% of total measured emissions were from 20% of the well pads, while in UGR the highest 20% of emitting well pads only contributed 54% of total emissions. The total mass of methane emitted as a percent of gross methane produced, termed throughput-normalized methane average (TNMA) and determined by bootstrapping measurements from each basin, varied widely between basins and was (95% CI): 0.09% (0.05-0.15%) in FV, 0.18% (0.12-0.29%) in UGR, 2.1% (1.1-3.9%) in DJ, and 2.8% (1.0-8.6%) in Uintah. Overall, wet-gas basins (UGR, DJ, Uintah) had higher TNMA emissions than the dry-gas FV at all ranges of production per well pad. Among wet basins, TNMA emissions had a strong negative correlation with average gas production per well pad, suggesting that consolidation of operations onto single pads may reduce normalized emissions (average number of wells per pad is 5.3 in UGR versus 1.3 in Uintah and 2.8 in DJ).

Improved Mechanistic Understanding of Natural Gas Methane Emissions from Spatially Resolved Aircraft Measurements.

Divergence in recent oil and gas related methane emission estimates between aircraft studies (basin total for a midday window) and emissions inventories (annualized regional and national statistics) indicate the need for better understanding the experimental design, including temporal and spatial alignment and interpretation of results. Our aircraft-based methane emission estimates in a major U.S. shale gas basin resolved from west to east show (i) similar spatial distributions for 2 days, (ii) strong spatial correlations with reported NG production (R2 = 0.75) and active gas well pad count (R2 = 0.81), and (iii) 2× higher emissions in the western half (normalized by gas production) despite relatively homogeneous dry gas and well characteristics. Operator reported hourly activity data show that midday episodic emissions from manual liquid unloadings (a routine operation in this basin and elsewhere) could explain ∼1/3 of the total emissions detected midday by the aircraft and ∼2/3 of the west-east difference in emissions. The 22% emission difference between both days further emphasizes that episodic sources can substantially impact midday methane emissions and that aircraft may detect daily peak emissions rather than daily averages that are generally employed in emissions inventories. While the aircraft approach is valid, quantitative, and independent, our study sheds new light on the interpretation of previous basin scale aircraft studies, and provides an improved mechanistic understanding of oil and gas related methane emissions.

Investigating detection probability of mobile survey solutions for natural gas pipeline leaks under different atmospheric conditions.

The 2015 Paris agreement aims to cut greenhouse gas emissions and keep global temperature rise below 2 °C above pre-industrial levels. Reducing CH4 emissions from leaking pipelines presents a relatively achievable objective. While walking and driving surveys are commonly used to detect leaks, the detection probability (DP) is poorly characterized. This study aims to investigate how leak rates, survey distance and speed, and atmospheric conditions affect the DP in controlled belowground conditions with release rates of 0.5-8.5 g min-1. Results show that DP is highly influenced by survey speed, atmospheric stability, and wind speed. The average DP in Pasquill-Gifford stability (PG) class A is 85% at a low survey speed (2-11 mph) and decreases to 68%, 63%, 65%, and 60% in PGSC B/C, D, E/F, and G respectively. It is generally less than 25% at a high survey speed (22-34 mph), regardless of stability conditions and leak rates. Using the measurement data, a validated DP model was further constructed and showed good performance (R2: 0.76). The options of modeled favorable weather conditions (i.e., PG stability class and wind speed) to have a high DP (e.g., >50%) are rapidly decreased with the increase in survey speed. Walking survey is applicable over a wider range of weather conditions, including PG stability class A to E/F and calm to medium winds (0-5 m s-1). A driving survey at a low speed (11 mph) can only be conducted under calm to low wind speed conditions (0-3 m s-1) to have an equivalent DP to a walking survey. Only calm wind conditions in PG A (0-1 m s-1) are appropriate for a high driving speed (34 mph). These findings showed that driving survey providers need to optimize the survey schemes to achieve a DP equivalence to the traditional walking survey.

Modeling air emissions from complex facilities at detailed temporal and spatial resolution: The Methane Emission Estimation Tool (MEET).

Recent attention to methane emissions from oil and gas infrastructure has increased interest in comparing measurements with inventory emission estimates. While measurement methods typically estimate emissions over a few periods that are seconds to hours in length, current inventory methods typically produce long-term average emission estimates. This temporal mis-alignment complicates comparisons and leads to underestimates in the uncertainty of measurement methods. This study describes a new temporally and spatially resolved inventory emission model (MEET), and demonstrates the model by application to compressor station emissions - the key facility type in midstream natural gas operations The study looks at three common facility measurement methods: tracer flux methods for measuring station emissions, the use of ethane-methane ratios for source attribution of basin-scale estimates, and the behavior of continuous monitoring for leak detection at stations. Simulation results indicate that measurement methods likely underestimate uncertainties in emission estimates by failing to account for the variability in normal facility emissions and variations in ethane/methane ratios. A tracer-based measurement campaign could estimate emissions outside the 95% confidence interval of annual emissions 30% of the time, while ethane/methane ratios could be mis-estimated by as much as 50%. Use of MEET also highlights the need to improve data reporting from measurement campaigns to better capture the temporal and spatial variation in observed emissions.

A Methane Emission Estimation Tool (MEET) for predictions of emissions from upstream oil and gas well sites with fine scale temporal and spatial resolution: Model structure and applications.

In comparing observation based methane emission estimates for oil and gas well sites to routine emissions reported in inventories, the time scale of the measurement should match the time scale over which the inventoried emissions are estimated. Since many measurements are of relatively short duration (seconds to hours), a tool is needed to estimate emissions over these time scales rather than the annual totals reported in most emission inventories. This work presents a tool for estimating routine emissions from oil and gas well sites at multiple time scales; emissions at well sites vary over time due to changes in oil and gas production rates, operating practices and operational modes at the sites. Distributions of routine emissions (expected and inventoried) from well sites are generally skewed, and the nature and degree to which the distributions are skewed depends on the time scales over which emissions are aggregated. Abnormal emissions can create additional skew in these distributions. At very short time scales (emissions aggregated over 1 min) case study distributions presented in this work are both skewed and bimodal, with the modes depending on whether liquid storage tanks are flashing at the time of the measurement and whether abnormal emissions are occurring. At longer time scales (emissions aggregated over 1 day) distributions of routine emissions simulated in this work can have multiple modes if short duration, high emission rate events, such as liquid unloadings or large abnormal emissions, occur at the site. Multiple applications of the methane emission estimation tool (MEET), developed in this work, are presented. These results emphasize the importance of developing detailed emission inventories, which incorporate operational data, when comparing measurements to routine emissions. The model described in this work supports such comparisons and is freely available.