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.

Carbon implications of marginal oils from market-derived demand shocks.

Expanded use of novel oil extraction technologies has increased the variability of petroleum resources and diversified the carbon footprint of the global oil supply1. Past life-cycle assessment (LCA) studies overlooked upstream emission heterogeneity by assuming that a decline in oil demand will displace average crude oil2. We explore the life-cycle greenhouse gas emissions impacts of marginal crude sources, identifying the upstream carbon intensity (CI) of the producers most sensitive to an oil demand decline (for example, due to a shift to alternative vehicles). We link econometric models of production profitability of 1,933 oilfields (~90% of the 2015 world supply) with their production CI. Then, we examine their response to a decline in demand under three oil market structures. According to our estimates, small demand shocks have different upstream CI implications than large shocks. Irrespective of the market structure, small shocks (-2.5% demand) displace mostly heavy crudes with ~25-54% higher CI than that of the global average. However, this imbalance diminishes as the shocks become bigger and if producers with market power coordinate their response to a demand decline. The carbon emissions benefits of reduction in oil demand are systematically dependent on the magnitude of demand drop and the global oil market structure.

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.

Large-Scale Controlled Experiment Demonstrates Effectiveness of Methane Leak Detection and Repair Programs at Oil and Gas Facilities.

Most jurisdictions around the globe use leak detection and repair (LDAR) programs to find and fix methane leaks from oil and gas operations. In this work, we empirically evaluate the efficacy of LDAR programs using a large-scale, bottom-up, randomized controlled field experiment across ∼200 oil and gas sites in Red Deer, Canada. We find that tanks are the single largest source of emissions, contributing to nearly 60% of the total emissions. The average number of leaks at treatment sites that underwent repair reduced by ∼50% compared to the control sites. Although control sites did not see a reduction in the number of leaks, emissions reduced by approximately 36%, suggesting potential impact of routine maintenance activities to find and fix large leaks. By tracking tags on leaking equipment over time, we find a high degree of persistence; leaks that are repaired remain fixed in follow-up surveys, while non-repaired leaks remain emitting at a similar rate, suggesting that any increase in observed leak emissions following LDAR surveys are likely from new leaks. Our results show that a focus on equipment and sites that are prone to high emissions, such as tanks and oil sites, is key to cost-effective mitigation.

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.

Quantifying Regional Methane Emissions in the New Mexico Permian Basin with a Comprehensive Aerial Survey.

Limiting emissions of climate-warming methane from oil and gas (O&G) is a major opportunity for short-term climate benefits. We deploy a basin-wide airborne survey of O&G extraction and transportation activities in the New Mexico Permian Basin, spanning 35 923 km2, 26 292 active wells, and over 15 000 km of natural gas pipelines using an independently validated hyperspectral methane point source detection and quantification system. The airborne survey repeatedly visited over 90% of the active wells in the survey region throughout October 2018 to January 2020, totaling approximately 98 000 well site visits. We estimate total O&G methane emissions in this area at 194 (+72/-68, 95% CI) metric tonnes per hour (t/h), or 9.4% (+3.5%/-3.3%) of gross gas production. 50% of observed emissions come from large emission sources with persistence-averaged emission rates over 308 kg/h. The fact that a large sample size is required to characterize the heavy tail of the distribution emphasizes the importance of capturing low-probability, high-consequence events through basin-wide surveys when estimating regional O&G methane emissions.

Greenhouse Gas Emissions of Western Canadian Natural Gas: Proposed Emissions Tracking for Life Cycle Modeling.

Natural gas (NG) produced in Western Canada is a major and growing source of Canada's energy and greenhouse gas (GHG) emissions portfolio. Despite recent progress, there is still only limited understanding of the sources and drivers of Western Canadian greenhouse gas (GHG) emissions. We conduct a case study of a production facility based on Seven Generation Energy Ltd.'s Western Canadian operations and an upstream NG emissions intensity model. The case study upstream emissions intensity is estimated to be 3.1-4.0 gCO2e/MJ NG compared to current best estimates of British Columbia (BC) emissions intensities of 6.2-12 gCO2e/MJ NG and a US average estimate of 15 gCO2e/MJ. The analysis reveals that compared to US studies, public GHG emissions data for Western Canada is insufficient as current public data satisfies only 50% of typical LCA model inputs. Company provided data closes most of these gaps (∼80% of the model inputs). We recommend more detailed data collection and presentation of government reported data such as a breakdown of vented and fugitive methane emissions by source. We propose a data collection template to facilitate improved GHG emissions intensity estimates and insight about potential mitigation strategies.

Evaluation of a proposal for reliable low-cost grid power with 100% wind, water, and solar.

A number of analyses, meta-analyses, and assessments, including those performed by the Intergovernmental Panel on Climate Change, the National Oceanic and Atmospheric Administration, the National Renewable Energy Laboratory, and the International Energy Agency, have concluded that deployment of a diverse portfolio of clean energy technologies makes a transition to a low-carbon-emission energy system both more feasible and less costly than other pathways. In contrast, Jacobson et al. [Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015) Proc Natl Acad Sci USA 112(49):15060-15065] argue that it is feasible to provide "low-cost solutions to the grid reliability problem with 100% penetration of WWS [wind, water and solar power] across all energy sectors in the continental United States between 2050 and 2055", with only electricity and hydrogen as energy carriers. In this paper, we evaluate that study and find significant shortcomings in the analysis. In particular, we point out that this work used invalid modeling tools, contained modeling errors, and made implausible and inadequately supported assumptions. Policy makers should treat with caution any visions of a rapid, reliable, and low-cost transition to entire energy systems that relies almost exclusively on wind, solar, and hydroelectric power.

Loxodonta Localizer: A Software Tool for Inferring the Provenance of African Elephants and Their Ivory Using Mitochondrial DNA.

Illegal hunting is a major threat to the elephants of Africa, with more elephants killed by poachers than die from natural causes. DNA from tusks has been used to infer the source populations for confiscated ivory, relying on nuclear genetic markers. However, mitochondrial DNA (mtDNA) sequences can also provide information on the geographic origins of elephants due to female elephant philopatry. Here, we introduce the Loxodonta Localizer (LL; www.loxodontalocalizer.org), an interactive software tool that uses a database of mtDNA sequences compiled from previously published studies to provide information on the potential provenance of confiscated ivory. A 316 bp control region sequence, which can be readily generated from DNA extracted from ivory, is used as a query. The software generates a listing of haplotypes reported among 1917 African elephants in 24 range countries, sorted in order of similarity to the query sequence. The African locations from which haplotype sequences have been previously reported are shown on a map. We demonstrate examples of haplotypes reported from only a single locality or country, examine the utility of the program in identifying elephants from countries with varying degrees of sampling, and analyze batches of confiscated ivory. The LL allows for the source of confiscated ivory to be assessed within days, using widely available molecular methods that do not depend on a particular platform or laboratory. The program enables identification of potential regions or localities from which elephants are being poached, with capacity for rapid identification of populations newly or consistently targeted by poachers.

Aerial Interyear Comparison and Quantification of Methane Emissions Persistence in the Bakken Formation of North Dakota, USA.

We performed an infrared optical gas imaging (OGI) survey by helicopter of hydrocarbon emissions in the Bakken formation of North Dakota. One year after an earlier survey of 682 well pads in September of 2014, the same helicopter crew resurveyed 353 well pads in 2015 to examine the persistence of emissions. Twenty-one newly producing well pads were added in the same sampling blocks. An instrumented aircraft was also used to quantify emissions from 33 plumes identified by aerial OGI. Well pads emitting methane and ethane in 2014 were far more likely to be emitting in 2015 than would be expected by chance; Monte Carlo simulations suggest >5σ deviation ( p < 0.0001) from random assignment of detectable emissions between survey years. Scaled up using basin-wide leakage estimates, the emissions quantified by aircraft are sufficient to explain previously observed basin-wide emissions of methane and ethane.

"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.

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.

Updating the U.S. Life Cycle GHG Petroleum Baseline to 2014 with Projections to 2040 Using Open-Source Engineering-Based Models.

The National Energy Technology Laboratory produced a well-to-wheels (WTW) life cycle greenhouse gas analysis of petroleum-based fuels consumed in the U.S. in 2005, known as the NETL 2005 Petroleum Baseline. This study uses a set of engineering-based, open-source models combined with publicly available data to calculate baseline results for 2014. An increase between the 2005 baseline and the 2014 results presented here (e.g., 92.4 vs 96.2 g CO2e/MJ gasoline, + 4.1%) are due to changes both in modeling platform and in the U.S. petroleum sector. An updated result for 2005 was calculated to minimize the effect of the change in modeling platform, and emissions for gasoline in 2014 were about 2% lower than in 2005 (98.1 vs 96.2 g CO2e/MJ gasoline). The same methods were utilized to forecast emissions from fuels out to 2040, indicating maximum changes from the 2014 gasoline result between +2.1% and -1.4%. The changing baseline values lead to potential compliance challenges with frameworks such as the Energy Independence and Security Act (EISA) Section 526, which states that Federal agencies should not purchase alternative fuels unless their life cycle GHG emissions are less than those of conventionally produced, petroleum-derived fuels.

Methane, Black Carbon, and Ethane Emissions from Natural Gas Flares in the Bakken Shale, North Dakota.

Incomplete combustion during flaring can lead to production of black carbon (BC) and loss of methane and other pollutants to the atmosphere, impacting climate and air quality. However, few studies have measured flare efficiency in a real-world setting. We use airborne data of plume samples from 37 unique flares in the Bakken region of North Dakota in May 2014 to calculate emission factors for BC, methane, ethane, and combustion efficiency for methane and ethane. We find no clear relationship between emission factors and aircraft-level wind speed or between methane and BC emission factors. Observed median combustion efficiencies for methane and ethane are close to expected values for typical flares according to the US EPA (98%). However, we find that the efficiency distribution is skewed, exhibiting log-normal behavior. This suggests incomplete combustion from flares contributes almost 1/5 of the total field emissions of methane and ethane measured in the Bakken shale, more than double the expected value if 98% efficiency was representative. BC emission factors also have a skewed distribution, but we find lower emission values than previous studies. The direct observation for the first time of a heavy-tail emissions distribution from flares suggests the need to consider skewed distributions when assessing flare impacts globally.

GHGfrack: An Open-Source Model for Estimating Greenhouse Gas Emissions from Combustion of Fuel during Drilling and Hydraulic Fracturing.

This paper introduces GHGfrack, an open-source engineering-based model that estimates energy consumption and associated GHG emissions from drilling and hydraulic fracturing operations. We describe verification and calibration of GHGfrack against field data for energy and fuel consumption. We run GHGfrack using data from 6927 wells in Eagle Ford and 4431 wells in Bakken oil fields. The average estimated energy consumption in Eagle Ford wells using lateral hole diameters of 8 (3)/4 and 6 (1)/8 in. are 2.25 and 2.73 TJ/well, respectively. The average estimated energy consumption in Bakken wells using hole diameters of 6 in. for horizontal section is 2.16 TJ/well. We estimate average greenhouse gas (GHG) emissions of 419 and 510 tonne of equivalent CO2 per well (tonne of CO2 eq/well) for the two aforementioned assumed geometries in Eagle Ford, respectively, and 417 tonne of CO2 eq/well for the case of Bakken. These estimates are limited only to GHG emissions from combustion of diesel fuel to supply energy only for rotation of drill string, drilling mud circulation, and fracturing pumps. Sensitivity analysis of the model shows that the top three key variables in driving energy intensity in drilling are the lateral hole diameter, drill pipe internal diameter, and mud flow rate. In hydraulic fracturing, the top three are lateral casing diameter, fracturing fluid volume, and length of the lateral.

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.

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.

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.

Uncertainty in regional-average petroleum GHG intensities: countering information gaps with targeted data gathering.

Recent efforts to model crude oil production GHG emissions are challenged by a lack of data. Missing data can affect the accuracy of oil field carbon intensity (CI) estimates as well as the production-weighted CI of groups ("baskets") of crude oils. Here we use the OPGEE model to study the effect of incomplete information on the CI of crude baskets. We create two different 20 oil field baskets, one of which has typical emissions and one of which has elevated emissions. Dispersion of CI estimates is greatly reduced in baskets compared to single crudes (coefficient of variation = 0.2 for a typical basket when 50% of data is learned at random), and field-level inaccuracy (bias) is removed through compensating errors (bias of ∼ 5% in above case). If a basket has underlying characteristics significantly different than OPGEE defaults, systematic bias is introduced through use of defaults in place of missing data. Optimal data gathering strategies were found to focus on the largest 50% of fields, and on certain important parameters for each field. Users can avoid bias (reduced to <1 gCO2/MJ in our elevated emissions basket) through strategies that only require gathering ∼ 10-20% of input data.