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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 38-86% of the time. When non-zero site-level emission rate estimates were provided, no linear correlation between release rate and reported emission rate estimate was observed. The average, aggregated across all PSN solutions during releases, shows 5% of mixing ratio readings at downwind sensors were greater than the site's baseline plus two standard deviations. Four of six 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.

Abstract. Increased natural gas production in recent years has spurred intense interest in methane (CH4) emissions associated with its production, gathering, processing, transmission and distribution. Gathering and processing facilities (G&P facilities) are unique in that the wide range of gas sources (shale, coal-bed, tight gas, conventional, etc.) results in a wide range of gas compositions, which in turn requires an array of technologies to prepare the gas for pipeline transmission and distribution. We present an overview and detailed description of the measurement method and analysis approach used during a 20-week field campaign studying CH4 emissions from the natural gas G&P facilities between October 2013 and April 2014. Dual tracer flux measurements and onsite observations were used to address the magnitude and origins of CH4 emissions from these facilities. The use of a second tracer as an internal standard revealed plume-specific uncertainties in the measured emission rates of 20–47%, depending upon plume classification. Combining downwind methane, ethane (C2H6), carbon monoxide (CO), carbon dioxide (CO2), and tracer gas measurements with onsite tracer gas release allows for quantification of facility emissions, and in some cases a more detailed picture of source locations.

Fugitive methane emissions from the oil and gas sector are typically addressed through periodic leak detection and repair surveys. These surveys, conducted manually using handheld leak detection technologies, are time-consuming. To improve the speed and cost-effectiveness of leak detection, technology developers are introducing innovative solutions using mobile platforms, close-range portable systems, and permanent installations. Many of these new approaches promise faster, cheaper, or more effective leak detection than conventional methods. However, ensuring mitigation targets are achieved requires demonstrating that alternative approaches are at least as effective in reducing emissions as current approaches – a concept known as emissions reduction equivalence. Here, we propose a five-stage framework for demonstrating equivalence that combines controlled testing, simulation modeling, and field trials. The framework was developed in consultation with operators, regulators, academics, solution providers, consultants, and non-profit groups from Canada and the U.S. We present the equivalence framework and discuss challenges to implementation.

The Smart Grid Initiative (SGI) encourages the integration of storage and peak-shaving technologies including plugin hybrid electric vehicles (PHEVs). PHEV sales in the US are expected to significantly increase primarily due to their potential to lower fuel costs by reducing fossil fuel consumption and to lower emissions. The rising penetration rate of these vehicles may however cause problems to utilities, especially as they may dramatically increase demand peaks if not managed properly. In this paper, a methodology is proposed to determine the impact of PHEV fleets with vehicle-to-grid (V2G) capabilities on electric distribution systems. The methodology relies on a probabilistic PHEV fleet characteristics model and a linear programming algorithm to determine the optimal charging patterns of the fleet vehicles, which are then used for utility peak-shaving purposes. Results for several scenarios show that a 30 % penetration level of V2G-capable PHEVs can be achieved without increasing peak load and without requiring any capacity update on the distribution infrastructure of the selected test system. Detailed results also show that daily load profiles would be modified for residential customers, and would stay closer to their average value than today.

Coordinated dual-tracer, aircraft-based, and direct component-level measurements were made at midstream natural gas gathering and boosting stations in the Fayetteville shale (Arkansas, USA). On-site component-level measurements were combined with engineering estimates to generate comprehensive facility-level methane emission rate estimates (“study on-site estimates (SOE)”) comparable to tracer and aircraft measurements. Combustion slip (unburned fuel entrained in compressor engine exhaust), which was calculated based on 111 recent measurements of representative compressor engines, accounts for an estimated 75% of cumulative SOEs at gathering stations included in comparisons. Measured methane emissions from regenerator vents on glycol dehydrator units were substantially larger than predicted by modelling software; the contribution of dehydrator regenerator vents to the cumulative SOE would increase from 1% to 10% if based on direct measurements. Concurrent measurements at 14 normally-operating facilities show relative agreement between tracer and SOE, but indicate that tracer measurements estimate lower emissions (regression of tracer to SOE = 0.91 (95% CI = 0.83–0.99), R2 = 0.89). Tracer and SOE 95% confidence intervals overlap at 11/14 facilities. Contemporaneous measurements at six facilities suggest that aircraft measurements estimate higher emissions than SOE. Aircraft and study on-site estimate 95% confidence intervals overlap at 3/6 facilities. The average facility level emission rate (FLER) estimated by tracer measurements in this study is 17–73% higher than a prior national study by Marchese et al.

Methane (CH4), a powerful greenhouse gas (GHG), has been identified as a key target for emission reduction in the Paris agreement, but it is not currently clear where efforts should be focused to make the greatest impact. Currently, activity data and standard emission factors (EF) are used to generate GHG emission inventories. Many of the EFs are globally uniform and do not account for regional variability in industrial or agricultural practices and/or regulation. Regional EFs can be derived from top-down emissions measurements and used to make bespoke regional GHG emission inventories that account for geopolitical and social variability. However, most large-scale top-down approaches campaigns require significant investment. To address this, lower-cost driving surveys (DS) have been identified as a viable alternative to more established methods. DSs can take top-down measurements of many emission sources in a relatively short period of time, albeit with a higher uncertainty. To investigate the use of a portable measurement system, a 2260 km DS was conducted throughout the Denver-Julesburg Basin (DJB). The DJB covers an area of 8000 km2 north of Denver, CO and is densely populated with CH4 emission sources, including oil and gas (O and G) operations, agricultural operations (AGOs), lakes and reservoirs. During the DS, 157 individual CH4 emission sources were detected; 51%, 43% and 4% of sources were AGOs, O and G operations, and natural sources, respectively. Methane emissions from each source were quantified using downwind concentration and meteorological data and AGOs and O and G operations represented nearly all the CH4 emissions in the DJB, accounting for 54% and 37% of the total emission, respectively. Operations with similar emission sources were grouped together and average facility emission estimates were generated. For agricultural sources, emissions from feedlot cattle, dairy cows and sheep were estimated at 5, 31 and 1 g CH4 head-1 h-1, all of which agreed with published values taken from focused measurement campaigns. Similarly, for O and G average emissions for well pads, compressor stations and gas processing plants (0.5, 14 and 110 kg CH4 facility-1 h-1) were in reasonable agreement with emission estimates from intensive measurement campaigns. A comparison of our basin wide O and G emissions to measurements taken a decade ago show a decrease of a factor of three, which can feasibly be explained by changes to O and G regulation over the past 10 years, while emissions from AGOs have remained constant over the same time period. Our data suggest that DSs could be a low-cost alternative to traditional measurement campaigns and used to screen many emission sources within a region to derive representative regionally specific and time-sensitive EFs. The key benefit of the DS is that many regions can be screened and emission reduction targets identified where regional EFs are noticeably larger than the regional, national or global averages.

Underground natural gas (NG) pipeline leakage can result in methane (CH4) buildup and migration through the soil. What is not well understood in such scenarios is how the soil conditions affect the gas migration behavior, particularly in regard to the relative contributions of specific soil properties such as soil moisture content. The objective of this study was to investigate the effects of soil properties on CH4 concentration and migration from leaking underground NG pipelines. Site characteristics such as surface cover and spatial dimensions, soil samples, and gas concentration data were collected from over 70 gas leakage sites across the United States using a standardized sampling method. Soil samples were collected from excavation sites that were 1.5′–5′ in depth. The collected soil samples were analyzed in the laboratory to measure the soil texture, permeability, and moisture. Statistical analysis was performed to evaluate the effects of soil properties on CH4 migration distance and concentration. Soil texture was consistent across geographic locations due to standardized pipeline backfill protocols, allowing for the analysis of gas concentration and transport data with respect to soil conditions. Soil moisture content was the dominant influence on the gas concentration and spreading distance. High soil moisture content was associated with reduced lateral diffusion and elevated concentrations near the leak point, whereas dry conditions were associated with reduced concentrations and greater spreading distance. Increasing soil moisture content reduced the lateral diffusion of CH4 into the soil due to water-induced tortuosity, resulting in elevated concentrations close to the leak point. Lateral migration of CH4 was suspected to be by diffusion, starting at 5 m from the leaks, while transport within the immediate vicinity of the leak was controlled by advection. These findings demonstrate a pronounced effect of soil moisture content and permeability on CH4 migration distance and concentration, providing key insight into the effects of soil conditions on NG migration and how to account for such effects in leak detection surveys.

Facility-level methane emissions were measured at 114 gathering facilities and 16 processing plants in the United States natural gas system. At gathering facilities, the measured methane emission rates ranged from 0.7 to 700 kg per hour (kg/h) (0.6 to 600 standard cubic feet per minute (scfm)). Normalized emissions (as a % of total methane throughput) were less than 1% for 85 gathering facilities and 19 had normalized emissions less than 0.1%. The range of methane emissions rates for processing plants was 3 to 600 kg/h (3 to 524 scfm), corresponding to normalized methane emissions rates <1% in all cases. The distributions of methane emissions, particularly for gathering facilities, are skewed. For example, 30% of gathering facilities contribute 80% of the total emissions. Normalized emissions rates are negatively correlated with facility throughput. The variation in methane emissions also appears driven by differences between inlet and outlet pressure, as well as venting and leaking equipment. Substantial venting from liquids storage tanks was observed at 20% of gathering facilities. Emissions rates at these facilities were, on average, around four times the rates observed at similar facilities without substantial venting.

Increased interest in greenhouse gas (GHG) emissions, including recent legislative action and voluntary programs, has increased attention on quantifying and ultimately reducing methane emissions from the natural gas supply chain. While inventories used for public or corporate GHG policies have traditionally utilized bottom-up (BU) methods to estimate emissions, the validity of such inventories has been questioned. Therefore, there is attention on utilizing full-facility measurements using airborne, satellite, or drone (top-down (TD)) techniques to inform, improve, or validate inventories. This study utilized full-facility estimates from two independent TD methods at 15 midstream natural gas facilities in the U.S.A., which were compared with a contemporaneous daily inventory assembled by the facility operator, employing comprehensive inventory methods. Estimates from the two TD methods statistically agreed in 2 of 28 paired measurements. Operator inventories, which included extensions to capture sources beyond regular inventory requirements and integration of local measurements, estimated significantly lower emissions than the TD estimates for 40 of 43 paired comparisons. Significant disagreement was observed at most facilities, both between the two TD methods and between the TD estimates and operator inventory. These findings have two implications. First, improving inventory estimates will require additional on-site or ground-based diagnostic screening and measurement of all sources. Second, the TD full-facility measurement methods need to undergo further testing, characterization, and potential improvement specifically tailored for complex midstream facilities.

This paper compares several electrical load models for estimating the efficiency of DC vs. AC distribution in micro-grids. Candidate models include energy balance, harmonic power flow, and time-domain modeling. Model results are compared with numerical studies and validated with experimental measurements. Based on quantitative and qualitative considerations, the most appropriate load modeling approach for larger-scale DC distribution efficiency studies is proposed.

This dataset includes input and output data used in the bottom-up model described in the associated manuscript and accompanying Supplemental Information Appendix. Additionally, the input dataset includes a ReadMe.txt describing it, and each of the output datasets includes a ReadMe.txt and a FileListing.txt describing their contents.

Natural gas (NG) leaks from below-ground pipelines pose safety, economic, and environmental hazards. Despite walking surveys using handheld methane (CH4) detectors to locate leaks, accurately triaging the severity of a leak remains challenging. It is currently unclear whether CH4 detectors used in walking surveys could be used to identify large leaks that require an immediate response. To explore this, we used above-ground downwind CH4 concentration measurements made during controlled emission experiments over a range of environmental conditions. These data were then used as the input to a novel modeling framework, the ESCAPE-1 model, to estimate the below-ground leak rates. Using 10-minute averaged CH4 mixing/meteorological data and filtering out wind speed < 2 m s-1/unstable atmospheric data, the ESCAPE-1 model estimates small leaks (0.2 kg CH4 h-1) and medium leaks (0.8 kg CH4 h-1) with a bias of -85%/+100% and -50%/+64%, respectively. Longer averaging (≥3 h) results in a 55% overestimation for small leaks and a 6% underestimation for medium leaks. These results suggest that as the wind speed increases or the atmosphere becomes more stable, the accuracy and precision of the leak rate calculated by the ESCAPE-1 model decrease. With an uncertainty of ±55%, our results show that CH4 mixing ratios measured using industry-standard detectors could be used to prioritize leak repairs.

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