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.

A cost comparison of various hourly-reliable and net-zero hydrogen production pathways in the United States

Hydrogen (H2) as an energy carrier may play a role in various hard-to-abate subsectors, but to maximize emission reductions, supplied hydrogen must be reliable, low-emission, and low-cost. Here, we build a model that enables direct comparison of the cost of producing net-zero, hourly-reliable hydrogen from various pathways. To reach net-zero targets, we assume upstream and residual facility emissions are mitigated using negative emission technologies. For the United States (California, Texas, and New York), model results indicate next-decade hybrid electricity-based solutions are lower cost ($2.02-$2.88/kg) than fossil-based pathways with natural gas leakage greater than 4% ($2.73-$5.94/kg). These results also apply to regions outside of the U.S. with a similar climate and electric grid. However, when omitting the net-zero emission constraint and considering the U.S. regulatory environment, electricity-based production only achieves cost-competitiveness with fossil-based pathways if embodied emissions of electricity inputs are not counted under U.S. Tax Code Section 45V guidance.

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.

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.

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.

Long-term survival after hyperthermic intraperitoneal chemotherapy using mitomycin C or oxaliplatin in colorectal cancer patients with synchronous peritoneal metastases: A nationwide comparative study

OBJECTIVES

In the Netherlands, limited variability exists in performance of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) among centers treating colorectal peritoneal metastases (PM), except for the intraperitoneal drug administration. This offers a unique opportunity to investigate any disparities in survival between the two most frequently used HIPEC regimens worldwide: mitomycin C (MMC) and oxaliplatin.

METHODS

This was a comparative, population-based cohort study of all Dutch patients diagnosed with synchronous colorectal PM who underwent CRS-HIPEC between 2014 and 2017. They were retrieved from the Netherlands Cancer Registry. Main outcome was overall survival (OS). The effect of the intraperitoneal drug on OS was investigated using multivariable Cox regression analysis.

RESULTS

In total, 297 patients treated between 2014 and 2017 were included. Among them, 177 (59.6%) received MMC and 120 (40.4%) received oxaliplatin. Only primary tumor location was different between the two groups: more left-sided colon in the Oxaliplatin group (47.5% vs. 33.3%, respectively, p=0.048). The 1-, 2- and 3-year OS were 84.6% vs. 85.8%, 61.6% vs. 63.9% and 44.7% vs. 53.5% in patients treated with MMC and oxaliplatin, respectively. Median OS was 30.7 months in the MMC group vs. 46.6 months in the oxaliplatin group (p=0.181). In multivariable analysis, no influence of intraperitoneal drug on survival was observed (adjusted HR 0.77 [0.53-1.13]).

CONCLUSIONS

Long-term survival between patients treated with either MMC or oxaliplatin during CRS-HIPEC was not significantly different.

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.

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.

"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.

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.

Water Use and Management in the Bakken Shale Oil Play in North Dakota

Oil and natural gas development in the Bakken shale play of North Dakota has grown substantially since 2008. This study provides a comprehensive overview and analysis of water quantity and management impacts from this development by (1) estimating water demand for hydraulic fracturing in the Bakken from 2008 to 2012; (2) compiling volume estimates for maintenance water, or brine dilution water; (3) calculating water intensities normalized by the amount of oil produced, or estimated ultimate recovery (EUR); (4) estimating domestic water demand associated with the large oil services population; (5) analyzing the change in wastewater volumes from 2005 to 2012; and (6) examining existing water sources used to meet demand. Water use for hydraulic fracturing in the North Dakota Bakken grew 5-fold from 770 million gallons in 2008 to 4.3 billion gallons in 2012. First-year wastewater volumes grew in parallel, from an annual average of 1,135,000 gallons per well in 2008 to 2,905,000 gallons in 2012, exceeding the mean volume of water used in hydraulic fracturing and surpassing typical 4-year wastewater totals for the Barnett, Denver, and Marcellus basins. Surprisingly, domestic water demand from the temporary oilfield services population in the region may be comparable to the regional water demand from hydraulic fracturing activities. Existing groundwater resources are inadequate to meet the demand for hydraulic fracturing, but there appear to be adequate surface water resources, provided that access is available.

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.

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.

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.

Open-source LCA tool for estimating greenhouse gas emissions from crude oil production using field characteristics

Existing transportation fuel cycle emissions models are either general and calculate nonspecific values of greenhouse gas (GHG) emissions from crude oil production, or are not available for public review and auditing. We have developed the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) to provide open-source, transparent, rigorous GHG assessments for use in scientific assessment, regulatory processes, and analysis of GHG mitigation options by producers. OPGEE uses petroleum engineering fundamentals to model emissions from oil and gas production operations. We introduce OPGEE and explain the methods and assumptions used in its construction. We run OPGEE on a small set of fictional oil fields and explore model sensitivity to selected input parameters. Results show that upstream emissions from petroleum production operations can vary from 3 gCO2/MJ to over 30 gCO2/MJ using realistic ranges of input parameters. Significant drivers of emissions variation are steam injection rates, water handling requirements, and rates of flaring of associated gas.

Using infrastructure optimization to reduce greenhouse gas emissions from oil sands extraction and processing

The Alberta oil sands are a significant source of oil production and greenhouse gas emissions, and their importance will grow as the region is poised for decades of growth. We present an integrated framework that simultaneously considers economic and engineering decisions for the capture, transport, and storage of oil sands CO(2) emissions. The model optimizes CO(2) management infrastructure at a variety of carbon prices for the oil sands industry. Our study reveals several key findings. We find that the oil sands industry lends itself well to development of CO(2) trunk lines due to geographic coincidence of sources and sinks. This reduces the relative importance of transport costs compared to nonintegrated transport systems. Also, the amount of managed oil sands CO(2) emissions, and therefore the CCS infrastructure, is very sensitive to the carbon price; significant capture and storage occurs only above 110$/tonne CO(2) in our simulations. Deployment of infrastructure is also sensitive to CO(2) capture decisions and technology, particularly the fraction of capturable CO(2) from oil sands upgrading and steam generation facilities. The framework will help stakeholders and policy makers understand how CCS infrastructure, including an extensive pipeline system, can be safely and cost-effectively deployed.

Variability and uncertainty in life cycle assessment models for greenhouse gas emissions from Canadian oil sands production

Because of interest in greenhouse gas (GHG) emissions from transportation fuels production, a number of recent life cycle assessment (LCA) studies have calculated GHG emissions from oil sands extraction, upgrading, and refining pathways. The results from these studies vary considerably. This paper reviews factors affecting energy consumption and GHG emissions from oil sands extraction. It then uses publicly available data to analyze the assumptions made in the LCA models to better understand the causes of variability in emissions estimates. It is found that the variation in oil sands GHG estimates is due to a variety of causes. In approximate order of importance, these are scope of modeling and choice of projects analyzed (e.g., specific projects vs industry averages); differences in assumed energy intensities of extraction and upgrading; differences in the fuel mix assumptions; treatment of secondary noncombustion emissions sources, such as venting, flaring, and fugitive emissions; and treatment of ecological emissions sources, such as land-use change-associated emissions. The GHGenius model is recommended as the LCA model that is most congruent with reported industry average data. GHGenius also has the most comprehensive system boundaries. Last, remaining uncertainties and future research needs are discussed.

Land use greenhouse gas emissions from conventional oil production and oil sands

Debates surrounding the greenhouse gas (GHG) emissions from land use of biofuels production have created a need to quantify the relative land use GHG intensity of fossil fuels. When contrasting land use GHG intensity of fossil fuel and biofuel production, it is the energy yield that greatly distinguishes the two. Although emissions released from land disturbed by fossil fuels can be comparable or higher than biofuels, the energy yield of oil production is typically 2-3 orders of magnitude higher, (0.33-2.6, 0.61-1.2, and 2.2 5.1 PJ/ha) for conventional oil production, oil sands surface mining, and in situ production, respectively). We found that land use contributes small portions of GHGs to life cycle emissions of California crude and in situ oil sands production ( <0.4% or < 0.4 gCO₂e/MJ crude refinery feedstock) and small to modest portions for Alberta conventional oil (0.1-4% or 0.1-3.4 gCO₂e/MJ) and surface mining of oil sands (0.9-11% or 0.8-10.2 gCO₂e/MJ).Our estimates are based on assumptions aggregated over large spatial and temporal scales and assuming 100% reclamation. Values on finer spatial and temporal scales that are relevant to policy targets need to account for site-specific information, the baseline natural and anthropogenic disturbance.

The climate impacts of bioenergy systems depend on market and regulatory policy contexts

Biomass can help reduce greenhouse gas (GHG) emissions by displacing petroleum in the transportation sector, by displacing fossil-based electricity, and by sequestering atmospheric carbon. Which use mitigates the most emissions depends on market and regulatory contexts outside the scope of attributional life cycle assessments. We show that bioelectricity's advantage over liquid biofuels depends on the GHG intensity of the electricity displaced. Bioelectricity that displaces coal-fired electricity could reduce GHG emissions, but bioelectricity that displaces wind electricity could increase GHG emissions. The electricity displaced depends upon existing infrastructure and policies affecting the electric grid. These findings demonstrate how model assumptions about whether the vehicle fleet and bioenergy use are fixed or free parameters constrain the policy questions an analysis can inform. Our bioenergy life cycle assessment can inform questions about a bioenergy mandate's optimal allocation between liquid fuels and electricity generation, but questions about the optimal level of bioenergy use require analyses with different assumptions about fixed and free parameters.

Converting oil shale to liquid fuels: energy inputs and greenhouse gas emissions of the Shell in situ conversion process

Oil shale is a sedimentary rock that contains kerogen, a fossil organic material. Kerogen can be heated to produce oil and gas (retorted). This has traditionally been a CO2-intensive process. In this paper, the Shell in situ conversion process (ICP), which is a novel method of retorting oil shale in place, is analyzed. The ICP utilizes electricity to heat the underground shale over a period of 2 years. Hydrocarbons are produced using conventional oil production techniques, leaving shale oil coke within the formation. The energy inputs and outputs from the ICP, as applied to oil shales of the Green River formation, are modeled. Using these energy inputs, the greenhouse gas (GHG) emissions from the ICP are calculated and are compared to emissions from conventional petroleum. Energy outputs (as refined liquid fuel) are 1.2-1.6 times greater than the total primary energy inputs to the process. In the absence of capturing CO2 generated from electricity produced to fuel the process, well-to-pump GHG emissions are in the range of 30.6-37.1 grams of carbon equivalent per megajoule of liquid fuel produced. These full-fuel-cycle emissions are 21%-47% larger than those from conventionally produced petroleum-based fuels.