Publications

Quantifying the Industrial Facility-Level Emission Rate of Methane in Various Segments of the Natural Gas Industry

An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study

This paper presents an artificial neural network (ANN) for forecasting the short-term electrical load of a university campus using real historical data from Colorado State University. A spatio-temporal ANN model with multiple weather variables as well as time identifiers, such as day of week and time of day, are used as inputs to the network presented. The choice of the number of hidden neurons in the network is made using statistical information and taking into account the point of diminishing returns. The performance of this ANN is quantified using three error metrics: the mean average percent error (MAPE); the error in the ability to predict the occurrence of the daily peak hour; and the difference in electrical energy consumption between the predicted and the actual values in a 24-hour period. These error measures provide a good indication of the constraints and applicability of these predictions. In the presence of some enabling technologies such as energy storage, rescheduling of non-critical loads, and availability of time of use (ToU) pricing, the possible DSM options that could stem from an accurate prediction of energy consumption of a campus include the identification of anomalous events as well the management of usage.

Learning from Failure: A Case Study of Repairing a Pico-hydropower System in Rwanda

In November 2019, a joint team from Colorado State University and the University of Rwanda intervened to repair a failed pico-hydropower plant in southwestern Rwanda. In a common outside-intervention model, the plant had been constructed by U.S. university team that later, for unknown reasons, couldn't provide additional intervention. The system had failed in 2016 after three years of operation. The qualitative analysis completed during the repair visit identified three primary issues which led to system failure: shortage of local management and technical skills resulting in poor maintenance and no financial reserves, poor local availability of common materials and tools needed for maintenance, and lack of community education to manage the plant. The analysis also includes an assessment of progress on these issues. While immediate technical issues have been resolved and training improved, fundamental operational processes require substantial additional work. The objective of this paper is to highlight, as is too infrequently the case, a failed village electrification project. Only by illuminating what isn't working can the development community learn the best practices.

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 38-86% 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.

A Review of Offshore Methane Quantification Methodologies

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h−1 from 188 platforms; 118 kg h−1 from 104 platforms using mass balance methods; 284 kg h−1 from 151 platforms using aircraft remote sensing; and 19,088 kg h−1 from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies.

Mechanistic Modeling of TEG Dehydrator Emissions in Oil and Gas Industry

This work presents a mechanistic modeling approach for simulating methane emissions from tri-ethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (< 0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades.

Determinants of the marginal willingness to pay for improved domestic water and irrigation in partially electrified Rwandan villages

Access to water for domestic and irrigation uses remains limited across Sub-Saharan Africa, particularly in rural areas. While the technical feasibility of implementing innovative technologies to improve water supply has been often evaluated, less is known about the drivers that motivate users to pay for obtaining the inherent benefits of improved water supply. In this article, the determinants of the marginal willingness to pay (mWTP) for improved domestic and irrigation water are investigated. For this purpose, a contingent valuation study is performed in rural Rwanda. This study uses survey data from 316 households situated in 8 villages, in which ~20 % of the households receive electricity thanks to microgrids powered by solar energy. It is found that the mWTP for improved domestic water supply is influenced by the proportion of children in the household, business ownership, and satisfaction with water quantity and quality. The mWTP for irrigation is determined by respondent education, business ownership and crop area planted. This paper contributes to the literature by allowing a comparison of the determinants of the mWTP for improved domestic water supply to the determinants of the mWTP for irrigation. Another contribution is to examine the mWTP for improved water infrastructure in partially electrified villages. Identifying areas with high marginal benefits from water can guide infrastructure investment and electrification efforts while improving well-being and increasing revenues in rural areas.

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.

Computational Fluid Dynamics-Based Modeling of Methane Flows Around Oil and Gas Equipment

Recent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane (CH4) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification depends on both measuring methane concentrations and converting them to emissions through a modeling framework. Currently, simple atmospheric dispersion models are primarily used to quantify emissions and concentrations, but these estimates are highly uncertain when quantifying emissions from complex aerodynamic sources, such as oil and gas facilities. This investigation used a CFD modeling approach, which can account for aerodynamic complexity but has hitherto not been used to model methane concentrations downwind of a methane release of a known rate, and compared it against in situ measurements. High-time-resolution (1 Hz) methane concentration and meteorological data were measured during experiments conducted at the METEC on 21 March and 11 July 2024. The METEC site configuration, measured wind data, and controlled emission rates were used as input for the CONVERGE CFD model to model downwind CH4 concentration. The modeling was carried out between 20 and 70 m, from two different points of release in two separate controlled-release experiments, one from a separator and another from a wellhead. In these experiments, we found that the CFD model could predict the CH4 concentrations downwind of the release to a good degree. The model was evaluated on multiple metrics to assess its performance in estimating methane concentrations at typical fence line distances (∼30 m). These results help us to understand external flows and the ability of CFD models to predict downwind concentrations in aerodynamically complex environments.

Deep Learning to Classify Methane Leak Size At Oil and Gas Facilities

Natural gas facility methane emissions: measurements by tracer flux ratio in two US natural gas producing basins

Methane (CH4) emission rates from a sample of natural gas facilities across industry sectors were quantified using the dual tracer flux ratio methodology. Measurements were conducted in study areas within the Fayetteville shale play, Arkansas (FV, Sept–Oct 2015, 53 facilities), and the Denver-Julesburg basin, Colorado, (DJ, Nov 2014, 21 facilities). Distributions of methane emission rates at facilities by type are computed and statistically compared with results that cover broader geographic regions in the US (Allen et al., 2013, Mitchell et al., 2015). DJ gathering station emission rates (kg CH4 hr–1) are lower, while FV gathering and production sites are statistically indistinguishable as compared to these multi-basin results. However, FV gathering station throughput-normalized emissions are statistically lower than multi-basin results (0.19% vs. 0.44%). This implies that the FV gathering sector is emitting less per unit of gas throughput than would be expected from the multi-basin distribution alone. The most common emission rate (i.e. mode of the distribution) for facilities in this study is 40 kg CH4 hr–1 for FV gathering stations, 1.0 kg CH4 hr–1 for FV production pads, and 11 kg CH4 hr–1 for DJ gathering stations. The importance of study design is discussed, including the benefits of site access and data sharing with industry and of a scientist dedicated to measurement coordination and site choice under evolving wind conditions.

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.

Why methane emissions matter to climate change: 5 questions answered

Performance of continuous emission monitoring solutions under single-blind controlled testing protocol.

Continuous emission monitoring (CM) solutions promise to accelerate fugitive methane emission mitigation in natural gas infrastructure by detecting large sources sooner than they would be detected in traditional leak surveys. Quantification by CM solutions has also been proposed as the foundation of measurement-based inventories. Herein we present the first published results of CM solutions tested using a consensus protocol. Single-blind testing was performed with controlled releases at a dedicated facility, using test conditions that were challenging but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicate detection limits (emission rate with 90% probability of detection) of 3-30 kg CH4/h, with 6 of 11 solutions in the range of 3-6 kg CH4/h, with high uncertainty. Of the 11 solutions tested, 6 provided emission rates estimates and for emission rate of 0.1-1 kg/h, the solutions' mean relative errors ranged from -44% to +586% with single estimates between -90% and +300% (all uncertainties stated as empirical 95% confidence intervals) with most solutions' upper uncertainty exceeding +900%. Solutions' mean relative error for emission rates >1 kg/h is -40% to +93% with two solutions achieving mean relative errors below 20%. In this range, all solutions exhibited single-estimate relative error from below -80% to values above +240%. When test results were simulated using emissions data from a portfolio of facilities, two solutions estimated emissions to within ±20%, while 4 estimated emissions at least double the true emission rate. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicate that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting. Additionally, given the rapid development of these solutions, additional implementation standards and regular testing will also be necessary.

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

Significance Our results demonstrate that access to high-resolution spatiotemporal activity data and multiscale, contemporaneous measurements is critical to understanding oil- and gas-related methane emissions. Careful consideration of all factors influencing methane emissions—including temporal variation—is necessary in scientific and policy discussions to develop effective strategies for mitigating greenhouse gas emissions from natural gas infrastructure.

Harmonic cancellation within AC low voltage distribution for a realistic office environment

Supplemental energy needed for wind integration

Storage developers and system operators remain interested in the potential of "firming" wind power at individual plants. The work presented here analyzes the energy required to firm individual plants, and the sensitivity of that energy to plant type, contract period, measurement period and forecast accuracy. The intent of this work is to provide quantitative design guidance to developers of storage solutions and other firming applications.

Unraveling Natural Gas Migration Rate and Extent from Leaking Underground Pipelines under Varying Environmental Conditions

Natural gas (NG) leakage from belowground pipelines is currently poorly understood, even though it is an area of safety, environmental, and economic concern. To date, there are limited studies on the transient behavior of NG, defined as the speed and maximum extent that gas travels belowground, and how this behavior changes with soil, environmental and leak characteristics. What is least identified is the interrelation between each controlling parameter, how to properly parameterize and characterize as well as the proper field application, specifically operator and first responder protocols. To address this gap, this work identifies key parameters influencing the transient behavior of leaked NG in the subsurface and opportunities to link this understanding to operator practice.  Though a three-year long series of over 150 controlled release experiments conducted at Colorado State University’s Methane Emission Technology Evaluation Center (METEC) and parallel numerical modeling we’ve investigated subsurface methane migration rates and extents and subsequent emission to the atmosphere. Experimental results were used to understand overall transient behavior both during and after terminating the leak. Numerical simulations were then used to extend experimental results to other conditions (e.g. additional soil types, surface conditions, and belowground infrastructure). Results demonstrate the impact of temporary rain and snow surface conditions on the extent and duration of leak transport, resulting in levels that pose heightened environmental and safety risks.  Furthermore, after leak termination, our findings demonstrate the isolated changes in the belowground migration time and the extent of leaked gas, driven by changes in surface and atmospheric conditions, a key point not consistently included in risk assessments or environmental emission rate calculations. While efforts to study a wider range of environmental conditions is underway, the findings of this study provide crucial insight to on identifying and prioritizing emissions from the perspective of both safety and the environment.