Publications

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emission and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large scale deployment of methane analyzers across the millions of emission sites is prohibitively expensive and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH4ERS) has been developed and comprises four sensing technologies: Metal Oxide (MOx,); Non-dispersion Infrared (NDIR); Integrated Infrared (INIR); and Tunable Diode Laser Absorption Spectrometer (TDLAS). This study describes the integration of these sensors to a single unit and test instrument response through calibration and downwind tests. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensor cannot respond fast enough to report concentrations that change on a sub-minute time frame. The NDIR sensor reported linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor re-ported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions and the WATCH4ERS units will be deployed in real-world environments to investigate the utility of each in the future.

With the decreased cost of power electronics, many traditionally alternating current (AC) loads have transitioned to use direct current (DC) at point of use. In this study, we compare the efficiency of a sample of common AC/DC power converters and estimate the potential energy consumption for common off-grid loads. Methods are based upon a larger Department of Energy project to characterize miscellaneous electric loads for commercial buildings. Using literature and measurements, we also estimate potential energy savings possible if common productive use appliances could be acquired in DC versions. Finally, we touch on international efforts to develop DC appliances and provide observations on methods required for characterizing energy efficiency.

Quantifying and controlling fugitive methane emissions from oil and gas facilities remains essential for addressing climate goals, but the costs associated with monitoring millions of production sites remain prohibitively expensive. Current thinking, supported by measurement and simple dispersion modelling, assumes single-digit parts-per-million instrumentation is required. To investigate instrument response, the inlets of three trace-methane (sub-ppm) analyzers were collocated on a facility designed to release gas of known composition at known flow rates between 0.4 and 5.2 kg CH4 h−1 from simulated oil and gas infrastructure. Methane mixing ratios were measured by each instrument at 1 Hertz resolution over nine hours. While mixing ratios reported by a cavity ring-down spectrometer (CRDS)-based instrument were on average 10.0 ppm (range 1.8 to 83 ppm), a mid-infrared laser absorption spectroscopy (MIRA)-based instrument reported short-lived mixing ratios far larger than expected (range 1.8 to 779 ppm) with a similar nine-hour average to the CRDS (10.1 ppm). We suggest the peaks detected by the MIRA are likely caused by a micrometeorological phenomenon, where vortex shedding has resulted in heterogeneous methane plumes which only the MIRA can observe. Further analysis suggests an instrument like the MIRA (an optical-cavity-based instrument with cavity size ≤10 cm3 measuring at ≥2 Hz with air flow rates in the order of ≤0.3 slpm at distances of ≤20 m from the source) but with a higher detection limit (25 ppm) could detect enough of the high-concentration events to generate representative 20 min-average methane mixing ratios. Even though development of a lower-cost, high-precision, high-accuracy instrument with a 25 ppm detection threshold remains a significant problem, this has implications for the use of instrumentation with higher detection thresholds, resulting in the reduction in cost to measure methane emissions and providing a mechanism for the widespread deployment of effective leak detection and repair programs for all oil and gas infrastructure.

It is crucial to reduce natural gas methane emissions, which can potentially offset the climate benefits of replacing coal with gas. Optical gas imaging (OGI) is a widely-used method to detect methane leaks, but is labor-intensive and cannot provide leak detection results without operators' judgment. In this paper, we develop a computer vision approach to OGI-based leak detection using convolutional neural networks (CNN) trained on methane leak images to enable automatic detection. First, we collect ~1 M frames of labeled video of methane leaks from different leaking equipment for building CNN model, covering a wide range of leak sizes (5.3-2051.6 gCH4/h) and imaging distances (4.6-15.6 m). Second, we examine different background subtraction methods to extract the methane plume in the foreground. Third, we then test three CNN model variants, collectively called GasNet, to detect plumes in videos taken at other pieces of leaking equipment. We assess the ability of GasNet to perform leak detection by comparing it to a baseline method that uses optical-flow based change detection algorithm. We explore the sensitivity of results to the CNN structure, with a moderate-complexity variant performing best across distances. We find that the detection accuracy can reach as high as 99%, the overall detection accuracy can exceed 95% for a case across all leak sizes and imaging distances. Binary detection accuracy exceeds 97% for large leaks (~710 gCH4/h) imaged closely (~5-7 m). At closer imaging distances (~5-10 m), CNN-based models have greater than 94% accuracy across all leak sizes. At farthest distances (~13-16 m), performance degrades rapidly, but it can achieve above 95% accuracy to detect large leaks (>950 gCH4/h). The GasNet-based computer vision approach could be deployed in OGI surveys to allow automatic vigilance of methane leak detection with high detection accuracy in the real world.

To align with climate initiatives, multiple reporting programs are transitioning from generic activity-based emission factors to site-specific measured emissions data to estimate greenhouse gas emissions at oil and gas facilities. This study contemporaneously deployed two top-down (TD) aerial methods across 14 midstream facilities, building upon previous research in the field. The methods produced multiple whole-facility estimates at each facility, resulting in 773 individual paired estimates (same facility, same day), and robust mean estimates for each facility. Mean estimates for each facility, aggregated across all facilities, differed by nearly 2:1 (49% [32% to 69%]). At 6 of 14 facilities, the methods produced mean estimates that differed by more than a factor of two. These data suggest that one or both methods did not produce accurate facility-level estimates at a majority of facilities and in aggregate across all facilities. The overall results are augmented with two case studies where TD estimates at two pre-selected facilities were coupled with comprehensive onsite measurements to understand the factors driving the divergence between TD and bottom-up (BU) emissions estimates. In 3 of 4 paired comparisons between the intensive onsite estimates and one of the TD methods, the intensive onsite surveys did not conclusively diagnose the difference in estimates. In these cases, our work suggests that the TD methods mis-estimate emissions an unknown fraction of the time, for unknown reasons. While two methods were selected for this study, it is unlikely that the issues identified here are confined to these two methods; similar issues may exist for other similar whole-facility methods on midstream and/or other facility types. These findings have important implications for the construction of voluntary and regulatory reporting programs that rely on emission estimates for reporting fees or penalties, or for studies using whole-facility estimates to aggregate TD emissions to basin or regional estimates.

Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas production sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm, with maximum values of 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML-derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.

Smart Energy Resources is the term used to define the new set of resources available to utilities to balance the supply-demand equation-renewable generation, energy storage, and consumer demand management. This chapter examines the potential impact of plug-in electric vehicles (PEVs) on the existing grid, describes methods of using smart grid technologies to alleviate foreseen problems, and investigates potential opportunities to enhance the performance of the electric grid using PEVs. Utilities and grid operators have assumed complete responsibility for meeting consumer demand, regardless of the pattern of the demand or how much it costs to provide the electricity. Due to the geographic location of renewable resources, much of the expected new renewable generation additions will be connected via one or two utility's transmission systems. The main obstacle to non-fossil resources (excluding nuclear that has its own challenges) is the intermittency of renewable generation, which limits the amount that can be integrated and compels system operators to schedule/dispatch expensive reserve units.

Standardized controlled testing of advanced methane detection technologies (solutions) has been identified as a step in demonstrating the emissions mitigation equivalence between these solutions and existing regulatory-approved leak detection and repair methods (e.g., ground-based optical gas imaging [OGI] camera survey) in the US and Canada. In this study, 12 solutions consisting of 4 handheld OGI cameras, 4 advanced handheld systems, and 4 mobile (automobile- and drone-based) solutions were tested under a single-blind controlled testing protocol at different periods between 2021 and 2023 at an outdoor test facility that simulates emissions from a simple, onshore North American production oil and gas (O&G) facility. Three solutions were tested again 3–12 months after the first test, using the same test protocol and facility to assess how performance changed over time. Results showed that handheld OGI cameras had comparable or better performance in terms of lower 90% probability of detection (DL90), false positive and negative fractions, and higher equipment unit-level localization performance compared to other categories of solutions tested. Advanced handheld solutions had comparable performance with the OGI cameras across all metrics except false positive fraction (much higher), while mobile solutions generally had shorter survey durations compared to other solutions. For solutions that tested twice, the performance of 2 of 3 solutions generally improved, illustrating the benefit of regular, comprehensive testing in the development of solutions. The different value propositions inferred from the various categories of solutions tested suggest that mobile solutions can rapidly survey larger areas to inform more targeted, follow-up inspections with handheld solutions. While advanced handheld solutions can be as effective as OGI cameras, mobile solutions have some difficulties to overcome to make them directly comparable or need to have a different use case.