Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

US oil and gas system emissions from nearly one million aerial site measurements.

As airborne methane surveys of oil and gas systems continue to discover large emissions that are missing from official estimates1-4, the true scope of methane emissions from energy production has yet to be quantified. We integrate approximately one million aerial site measurements into regional emissions inventories for six regions in the USA, comprising 52% of onshore oil and 29% of gas production over 15 aerial campaigns. We construct complete emissions distributions for each, employing empirically grounded simulations to estimate small emissions. Total estimated emissions range from 0.75% (95% confidence interval (CI) 0.65%, 0.84%) of covered natural gas production in a high-productivity, gas-rich region to 9.63% (95% CI 9.04%, 10.39%) in a rapidly expanding, oil-focused region. The six-region weighted average is 2.95% (95% CI 2.79%, 3.14%), or roughly three times the national government inventory estimate5. Only 0.05-1.66% of well sites contribute the majority (50-79%) of well site emissions in 11 out of 15 surveys. Ancillary midstream facilities, including pipelines, contribute 18-57% of estimated regional emissions, similarly concentrated in a small number of point sources. Together, the emissions quantified here represent an annual loss of roughly US$1 billion in commercial gas value and a US$9.3 billion annual social cost6. Repeated, comprehensive, regional remote-sensing surveys offer a path to detect these low-frequency, high-consequence emissions for rapid mitigation, incorporation into official emissions inventories and a clear-eyed assessment of the most effective emission-finding technologies for a given region.

Excess methane emissions from shallow water platforms elevate the carbon intensity of US Gulf of Mexico oil and gas production.

The Gulf of Mexico is the largest offshore fossil fuel production basin in the United States. Decisions on expanding production in the region legally depend on assessments of the climate impact of new growth. Here, we collect airborne observations and combine them with previous surveys and inventories to estimate the climate impact of current field operations. We evaluate all major on-site greenhouse gas emissions, carbon dioxide (CO2) from combustion, and methane from losses and venting. Using these findings, we estimate the climate impact per unit of energy of produced oil and gas (the carbon intensity). We find high methane emissions (0.60 Tg/y [0.41 to 0.81, 95% confidence interval]) exceeding inventories. This elevates the average CI of the basin to 5.3 g CO2e/MJ [4.1 to 6.7] (100-y horizon) over twice the inventories. The CI across the Gulf varies, with deep water production exhibiting a low CI dominated by combustion emissions (1.1 g CO2e/MJ), while shallow federal and state waters exhibit an extraordinarily high CI (16 and 43 g CO2e/MJ) primarily driven by methane emissions from central hub facilities (intermediaries for gathering and processing). This shows that production in shallow waters, as currently operated, has outsized climate impact. To mitigate these climate impacts, methane emissions in shallow waters must be addressed through efficient flaring instead of venting and repair, refurbishment, or abandonment of poorly maintained infrastructure. We demonstrate an approach to evaluate the CI of fossil fuel production using observations, considering all direct production emissions while allocating to all fossil products.

Understanding variability in petroleum jet fuel life cycle greenhouse gas emissions to inform aviation decarbonization.

A pressing challenge facing the aviation industry is to aggressively reduce greenhouse gas emissions in the face of increasing demand for aviation fuels. Climate goals such as carbon-neutral growth from 2020 onwards require continuous improvements in technology, operations, infrastructure, and most importantly, reductions in aviation fuel life cycle emissions. The Carbon Offsetting Scheme for International Aviation of the International Civil Aviation Organization provides a global market-based measure to group all possible emissions reduction measures into a joint program. Using a bottom-up, engineering-based modeling approach, this study provides the first estimates of life cycle greenhouse gas emissions from petroleum jet fuel on regional and global scales. Here we show that not all petroleum jet fuels are the same as the country-level life cycle emissions of petroleum jet fuels range from 81.1 to 94.8 gCO2e MJ-1, with a global volume-weighted average of 88.7 gCO2e MJ-1. These findings provide a high-resolution baseline against which sustainable aviation fuel and other emissions reduction opportunities can be prioritized to achieve greater emissions reductions faster.

Low-Cost Representative Sampling for a Natural Gas Distribution System in Transition.

Natural gas distribution systems within municipalities supply a substantial fraction of energy consumed in the United States. As decarbonization of the natural gas system necessitates new modes of operation outside original design purposes, for example, increased hydrogen or biogas blending, it becomes increasingly important to understand in advance how existing infrastructure will respond to these changes. Such an analysis will require detailed information about the existing asset base, such as local soil composition, plastic type, and other characteristics that are not systematically tracked at present or have substantial missing data. Opportunistic sampling, for example, collecting measurements at assets that are already undergoing maintenance, has the potential to substantially reduce the cost of gathering such data but only if the results are representative of the full asset base. To assess prospects for such an approach, we employ a dataset including the entire service line and leak database from a large natural gas distribution utility (∼66,700 km of service pipelines and over 530,000 leaks over decades of observations). This dataset shows that service lines affected by excavation damage produce an approximately random sample of plastic and steel service lines, with similar distributions of component age, operating pressure, and pipeline diameter, as well as a relatively uniform spatial distribution. This means that opportunistic measurements at these locations will produce a first-order estimate of the relative prevalence of key characteristics across the utility's full asset base of service lines. We employ this approach to estimate the plastic type, which is unknown for roughly 80% of plastic service lines in the database. We also find that while 32% of leaks across all components occur in threaded steel junctions, excavation damage accounts for 75% of hazardous grade 1 leaks in plastic service lines and corrosion accounts for 47% in steel service lines. Insights from this sampling approach can thus help natural gas utilities collect the data they need to ensure a safe and reliable transition to a lower-emission system.

Methane Emissions from Natural Gas Gathering Pipelines in the Permian Basin.

The rapid reduction of methane emissions, especially from oil and gas (O&G) operations, is a critical part of slowing global warming. However, few studies have attempted to specifically characterize emissions from natural gas gathering pipelines, which tend to be more difficult to monitor on the ground than other forms of O&G infrastructure. In this study, we use methane emission measurements collected from four recent aerial campaigns in the Permian Basin, the most prolific O&G basin in the United States, to estimate a methane emission factor for gathering lines. From each campaign, we calculate an emission factor between 2.7 (+1.9/-1.8, 95% confidence interval) and 10.0 (+6.4/-6.2) Mg of CH4 year-1 km-1, 14-52 times higher than the U.S. Environmental Protection Agency's national estimate for gathering lines and 4-13 times higher than the highest estimate derived from a published ground-based survey of gathering lines. Using Monte Carlo techniques, we demonstrate that aerial data collection allows for a greater sample size than ground-based data collection and therefore more comprehensive identification of emission sources that comprise the heavy tail of methane emissions distributions. Our results suggest that pipeline emissions are underestimated in current inventories and highlight the importance of a large sample size when calculating basinwide pipeline emission factors.

Inefficient and unlit natural gas flares both emit large quantities of methane.

Flaring is widely used by the fossil fuel industry to dispose of natural gas. Industry and governments generally assume that flares remain lit and destroy methane, the predominant component of natural gas, with 98% efficiency. Neither assumption, however, is based on real-world observations. We calculate flare efficiency using airborne sampling across three basins responsible for >80% of US flaring and combine these observations with unlit flare prevalence surveys. We find that both unlit flares and inefficient combustion contribute comparably to ineffective methane destruction, with flares effectively destroying only 91.1% (90.2, 91.8; 95% confidence interval) of methane. This represents a fivefold increase in methane emissions above present assumptions and constitutes 4 to 10% of total US oil and gas methane emissions, highlighting a previously underappreciated methane source and mitigation opportunity.

Closing the methane gap in US oil and natural gas production emissions inventories.

Methane (CH4) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH4 emissions at different spatial scales are ~1.5-2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH4 mitigation policy priorities.

Assessment of methane emissions from the U.S. oil and gas supply chain.

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO2 from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

Are Optical Gas Imaging Technologies Effective For Methane Leak Detection?

Concerns over mitigating methane leakage from the natural gas system have become ever more prominent in recent years. Recently, the U.S. Environmental Protection Agency proposed regulations requiring use of optical gas imaging (OGI) technologies to identify and repair leaks. In this work, we develop an open-source predictive model to accurately simulate the most common OGI technology, passive infrared (IR) imaging. The model accurately reproduces IR images of controlled methane release field experiments as well as reported minimum detection limits. We show that imaging distance is the most important parameter affecting IR detection effectiveness. In a simulated well-site, over 80% of emissions can be detected from an imaging distance of 10 m. Also, the presence of "superemitters" greatly enhance the effectiveness of IR leak detection. The minimum detectable limits of this technology can be used to selectively target "superemitters", thereby providing a method for approximate leak-rate quantification. In addition, model results show that imaging backdrop controls IR imaging effectiveness: land-based detection against sky or low-emissivity backgrounds have higher detection efficiency compared to aerial measurements. Finally, we show that minimum IR detection thresholds can be significantly lower for gas compositions that include a significant fraction nonmethane hydrocarbons.

Methane Leaks from Natural Gas Systems Follow Extreme Distributions.

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.

Aerial Surveys of Elevated Hydrocarbon Emissions from Oil and Gas Production Sites.

Oil and gas (O&G) well pads with high hydrocarbon emission rates may disproportionally contribute to total methane and volatile organic compound (VOC) emissions from the production sector. In turn, these emissions may be missing from most bottom-up emission inventories. We performed helicopter-based infrared camera surveys of more than 8000 O&G well pads in seven U.S. basins to assess the prevalence and distribution of high-emitting hydrocarbon sources (detection threshold ∼ 1-3 g s(-1)). The proportion of sites with such high-emitting sources was 4% nationally but ranged from 1% in the Powder River (Wyoming) to 14% in the Bakken (North Dakota). Emissions were observed three times more frequently at sites in the oil-producing Bakken and oil-producing regions of mixed basins (p < 0.0001, χ(2) test). However, statistical models using basin and well pad characteristics explained 14% or less of the variance in observed emission patterns, indicating that stochastic processes dominate the occurrence of high emissions at individual sites. Over 90% of almost 500 detected sources were from tank vents and hatches. Although tank emissions may be partially attributable to flash gas, observed frequencies in most basins exceed those expected if emissions were effectively captured and controlled, demonstrating that tank emission control systems commonly underperform. Tanks represent a key mitigation opportunity for reducing methane and VOC emissions.

Comparing Natural Gas Leakage Detection Technologies Using an Open-Source "Virtual Gas Field" Simulator.

We present a tool for modeling the performance of methane leak detection and repair programs that can be used to evaluate the effectiveness of detection technologies and proposed mitigation policies. The tool uses a two-state Markov model to simulate the evolution of methane leakage from an artificial natural gas field. Leaks are created stochastically, drawing from the current understanding of the frequency and size distributions at production facilities. Various leak detection and repair programs can be simulated to determine the rate at which each would identify and repair leaks. Integrating the methane leakage over time enables a meaningful comparison between technologies, using both economic and environmental metrics. We simulate four existing or proposed detection technologies: flame ionization detection, manual infrared camera, automated infrared drone, and distributed detectors. Comparing these four technologies, we found that over 80% of simulated leakage could be mitigated with a positive net present value, although the maximum benefit is realized by selectively targeting larger leaks. Our results show that low-cost leak detection programs can rely on high-cost technology, as long as it is applied in a way that allows for rapid detection of large leaks. Any strategy to reduce leakage should require a careful consideration of the differences between low-cost technologies and low-cost programs.

Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production.

Studies of the energy return on investment (EROI) for oil production generally rely on aggregated statistics for large regions or countries. In order to better understand the drivers of the energy productivity of oil production, we use a novel approach that applies a detailed field-level engineering model of oil and gas production to estimate energy requirements of drilling, producing, processing, and transporting crude oil. We examine 40 global oilfields, utilizing detailed data for each field from hundreds of technical and scientific data sources. Resulting net energy return (NER) ratios for studied oil fields range from ≈2 to ≈100 MJ crude oil produced per MJ of total fuels consumed. External energy return (EER) ratios, which compare energy produced to energy consumed from external sources, exceed 1000:1 for fields that are largely self-sufficient. The lowest energy returns are found to come from thermally-enhanced oil recovery technologies. Results are generally insensitive to reasonable ranges of assumptions explored in sensitivity analysis. Fields with very large associated gas production are sensitive to assumptions about surface fluids processing due to the shifts in energy consumed under different gas treatment configurations. This model does not currently include energy invested in building oilfield capital equipment (e.g., drilling rigs), nor does it include other indirect energy uses such as labor or services.

Embodied Energy and GHG Emissions from Material Use in Conventional and Unconventional Oil and Gas Operations.

Environmental impacts embodied in oilfield capital equipment have not been thoroughly studied. In this paper, we present the first open-source model which computes the embodied energy and greenhouse gas (GHG) emissions associated with materials consumed in constructing oil and gas wells and associated infrastructure. The model includes well casing, wellbore cement, drilling mud, processing equipment, gas compression, and transport infrastructure. Default case results show that consumption of materials in constructing oilfield equipment consumes ∼0.014 MJ of primary energy per MJ of oil produced, and results in ∼1.3 gCO2-eq GHG emissions per MJ (lower heating value) of crude oil produced, an increase of 15% relative to upstream emissions assessed in earlier OPGEE model versions, and an increase of 1-1.5% of full life cycle emissions. A case study of a hydraulically fractured well in the Bakken formation of North Dakota suggests lower energy intensity (0.011 MJ/MJ) and emissions intensity (1.03 gCO2-eq/MJ) due to the high productivity of hydraulically fractured wells. Results are sensitive to per-well productivity, the complexity of wellbore casing design, and the energy and emissions intensity per kg of material consumed.

Well-to-Wheels Greenhouse Gas Emissions of Canadian Oil Sands Products: Implications for U.S. Petroleum Fuels.

Greenhouse gas (GHG) regulations affecting U.S. transportation fuels require holistic examination of the life-cycle emissions of U.S. petroleum feedstocks. With an expanded system boundary that included land disturbance-induced GHG emissions, we estimated well-to-wheels (WTW) GHG emissions of U.S. production of gasoline and diesel sourced from Canadian oil sands. Our analysis was based on detailed characterization of the energy intensities of 27 oil sands projects, representing industrial practices and technological advances since 2008. Four major oil sands production pathways were examined, including bitumen and synthetic crude oil (SCO) from both surface mining and in situ projects. Pathway-average GHG emissions from oil sands extraction, separation, and upgrading ranged from ∼6.1 to ∼27.3 g CO2 equivalents per megajoule (in lower heating value, CO2e/MJ). This range can be compared to ∼4.4 g CO2e/MJ for U.S. conventional crude oil recovery. Depending on the extraction technology and product type output of oil sands projects, the WTW GHG emissions for gasoline and diesel produced from bitumen and SCO in U.S. refineries were in the range of 100-115 and 99-117 g CO2e/MJ, respectively, representing, on average, about 18% and 21% higher emissions than those derived from U.S. conventional crudes. WTW GHG emissions of gasoline and diesel derived from diluted bitumen ranged from 97 to 103 and 96 to 104 g CO2e/MJ, respectively, showing the effect of diluent use on fuel emissions.

Reproducibility of LCA models of crude oil production.

Scientific models are ideally reproducible, with results that converge despite varying methods. In practice, divergence between models often remains due to varied assumptions, incompleteness, or simply because of avoidable flaws. We examine LCA greenhouse gas (GHG) emissions models to test the reproducibility of their estimates for well-to-refinery inlet gate (WTR) GHG emissions. We use the Oil Production Greenhouse gas Emissions Estimator (OPGEE), an open source engineering-based life cycle assessment (LCA) model, as the reference model for this analysis. We study seven previous studies based on six models. We examine the reproducibility of prior results by successive experiments that align model assumptions and boundaries. The root-mean-square error (RMSE) between results varies between ∼1 and 8 g CO2 eq/MJ LHV when model inputs are not aligned. After model alignment, RMSE generally decreases only slightly. The proprietary nature of some of the models hinders explanations for divergence between the results. Because verification of the results of LCA GHG emissions is often not possible by direct measurement, we recommend the development of open source models for use in energy policy. Such practice will lead to iterative scientific review, improvement of models, and more reliable understanding of emissions.

Uncertainty of oil field GHG emissions resulting from information gaps: a Monte Carlo approach.

Regulations on greenhouse gas (GHG) emissions from liquid fuel production generally work with incomplete data about oil production operations. We study the effect of incomplete information on estimates of GHG emissions from oil production operations. Data from California oil fields are used to generate probability distributions for eight oil field parameters previously found to affect GHG emissions. We use Monte Carlo (MC) analysis on three example oil fields to assess the change in uncertainty associated with learning of information. Single factor uncertainties are most sensitive to ignorance about water-oil ratio (WOR) and steam-oil ratio (SOR), resulting in distributions with coefficients of variation (CV) of 0.1-0.9 and 0.5, respectively. Using a combinatorial uncertainty analysis, we find that only a small number of variables need to be learned to greatly improve on the accuracy of MC mean. At most, three pieces of data are required to reduce bias in MC mean to less than 5% (absolute). However, the parameters of key importance in reducing uncertainty depend on oil field characteristics and on the metric of uncertainty applied. Bias in MC mean can remain after multiple pieces of information are learned, if key pieces of information are left unknown.

Peak oil demand: the role of fuel efficiency and alternative fuels in a global oil production decline.

Some argue that peak conventional oil production is imminent due to physical resource scarcity. We examine the alternative possibility of reduced oil use due to improved efficiency and oil substitution. Our model uses historical relationships to project future demand for (a) transport services, (b) all liquid fuels, and (c) substitution with alternative energy carriers, including electricity. Results show great increases in passenger and freight transport activity, but less reliance on oil. Demand for liquids inputs to refineries declines significantly after 2070. By 2100 transport energy demand rises >1000% in Asia, while flattening in North America (+23%) and Europe (-20%). Conventional oil demand declines after 2035, and cumulative oil production is 1900 Gbbl from 2010 to 2100 (close to the U.S. Geological Survey median estimate of remaining oil, which only includes projected discoveries through 2025). These results suggest that effort is better spent to determine and influence the trajectory of oil substitution and efficiency improvement rather than to focus on oil resource scarcity. The results also imply that policy makers should not rely on liquid fossil fuel scarcity to constrain damage from climate change. However, there is an unpredictable range of emissions impacts depending on which mix of substitutes for conventional oil gains dominance-oil sands, electricity, coal-to-liquids, or others.