New open-source lifecycle analysis tool for oil production using field characteristics

Schematic chart showing included stages within OPGEE. El Houjeiri et al., Supplemental Information. Click to enlarge.

A team from Stanford University and the California Air Resources Board (ARB) has developed a new open-source lifecycle analysis (LCA) tool for modeling the greenhouse gas emissions of oil and gas production using characteristics of specific fields and associated production pathways. The team describes the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) in a paper in the ACS journal Environmental Science & Technology.

Existing transportation fuel cycle emissions models are either broad-i.e., lacking process-level detail for any particular fuel pathway-and calculate nonspecific values of greenhouse gas (GHG) emissions from crude oil production, or are not available for public review and auditing, the authors note.

Emissions of greenhouse gases (GHGs) from crude oil production vary significantly depending on production practices and crude oil qualities. The use of energy-intensive secondary and tertiary recovery technologies can have significant impacts on emissions. Other major factors are venting, flaring and fugitive (VFF) emissions, which are difficult to measure and estimate. Previous studies show that upstream, well-to-refinery gate (WTR) emissions vary by a factor of 10 from low emissions to high emissions fields. This variability highlights the importance of having the capability to assess the different types of crude oil production operations and under different conditions.

Regulatory approaches, such as the California Low Carbon Fuel Standard (LCFS) and European Fuel Quality Directive (EU FQD), seek to regulate the life cycle GHG emissions for transport fuels.

...To advance the modeling of crude oil production GHGs in a transparent manner, the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) has been developed. OPGEE is built with the goals of achieving more accuracy and better transparency in the assessment of life cycle GHG emissions from crude oil production. OPGEE calculates the energy use and emissions from crude oil production using engineering fundamentals of petroleum production and processing. This allows the model to flexibly estimate emissions from a variety of oil production emissions sources.

-El-Houjeiri et al.

In their paper, Hassan El-Houjeiri and Adam Brandt from Stanford, and James Duffy from ARB, introduce OPGEE and its structure, modeling methods, and data sources, then run it in default mode and on a small set of fictional fields (based on real California fields) selected to have varying characteristics and meant to represent a variety of possible operations. These serve to anchor the sensitivity analysis. The results show the GHG emissions breakdown and the sensitivity of emissions to selected input parameters.

The functional unit of OPGEE is 1 MJ of crude petroleum delivered to the refinery entrance (a well-to-refinery, or WTR system boundary), with emissions presented as gCO₂ equiv GHGs per MJ of crude at the refinery gate. This functional unit is held constant across different production processes included in OPGEE. The energy content of crude oil at the refinery gate is calculated based on API gravity (no account of effects of other crude oil characteristics such as sulfur content). OPGEE defaults to lower heating value (LHV) basis for all calculations, but model results can also be presented on higher heating value (HHV) basis.

Basic structure of OPGEE. Credit: ACS, El-Houjeiri et al. Click to enlarge.

OPGEE calculations use a bottom-up engineering-based approach. OPGEE relies on dozens of calculations across all stages of oil production, processing and transport.

Data for the four fictional fields used in the paper (A, B, C, D) are derived from the online production and injection database and technical reports from the California Department of Conservation, Division of Oil, Gas, and Geothermal Resources (DOGGR).

Field A uses steam injection to decrease crude viscosity. Field B is characterized by very high water-oil ratio (WOR), which represents an inefficient lifting process and significant energy use to manage large amounts of water at the surface (e.g., treatment and re-injection). Field C is characterized by average depth and moderate WOR. Field D is characterized by low depth, low WOR, and higher gas‚àíoil ratio (GOR). The "generic" case uses only the default parameters used to run OPGEE when no data are available.

WTR GHG intensity of California fields compared to OPGEE default. Field A has high GHG because of the use of energy-intensive steam injection. Field B is depleted, with WOR = 40 (e.g., it produces 40 bbl of water per bbl oil). Lifting and handling this amount of fluid is inefficient and consumes large amounts of energy. The water produced is assumed to be re-injected into the reservoir to maintain pressure, increasing the energy intensity of production. Fields C and D have relatively low GHG intensity because they do not use energy-intensive secondary and tertiary production technologies and have moderate to low WOR. Click to enlarge. Credit: ACS, El-Houjeiri et al.

The researchers explored variation in GHG outcomes due to WOR; field depth; oil production volume; steam-oil ratio (SOR); application of a heater/treater in surface oil‚àíwater separation; and flaring rate. OPGEE found that that upstream emissions from petroleum production operations can vary from 3 gCO₂/MJ to more than 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.

Results from OPGEE show clear evidence that assuming a single value for the GHG intensity of oil production is problematic because of significant variation in emissions from different operations. This is particularly the case for regulations aiming to reduce WTW GHG intensity of fuels. Future efforts to better understand and characterize this variation are clearly required. Additional efforts will also focus on improving data availability and the data basis for model defaults.

Future work on OPGEE will address scope limitations and coverage of technologies. Coverage will expand to include oil sands operations, as well as heavy oil and other EOR technologies. Supporting technologies, such as hydraulic fracturing and stimulation, will be included to better represent modern production practices.

-El-Houjeiri et al.

The work was funded by ARB.

Resources

• Hassan M. El-Houjeiri, Adam R. Brandt, and James E. Duffy (2013) Open-Source LCA Tool for Estimating Greenhouse Gas Emissions from Crude Oil Production Using Field Characteristics. Environmental Science & Technology doi: 10.1021/es304570m

http://www.greencarcongress.com/2013/05/new-open-source-lifecycle-anal

New Argonne lifecycle analysis of bioethanol pathways finds corn ethanol can reduce GHG emissions relative to gasoline by 19-48%; long-term, cellulosic offers the most benefits

Well-to-wheels results for greenhouse gas emissions in CO2e for six pathways. Source: Wang et al. Click to enlarge.

A new lifecycle analysis of five bioethanol production pathways by a team from Argonne National Laboratory led by Dr. Michael Wang found that, relative to petroleum gasoline, ethanol from corn; sugarcane; corn stover; switchgrass; and miscanthus can reduce lifecycle greenhouse gas (GHG) emissions [P10-P90 (P50)] by 19-48% (34%); 40-62% (51%); 90-103% (96%); 77-97% (88%); and 101-115% (108%), respectively when including land use change emissions. They researchers reported similar trends with regard to fossil energy benefits for the five bioethanol pathways. An open access paper on the study in published in the journal Environmental Research Letters.

While the results for cellulosic ethanol (stover, switchgrass and miscanthus) are in line with recent studies, and the findings for sugarcane ethanol are only slightly lower than other similar studies, the results for corn ethanol are in sharp contrast to other studies predicting that corn ethanol would have a greater life-cycle GHG impact than gasoline, the authors noted.

Bioethanol is the biofuel that is produced and consumed the most globally. The US is the dominant producer of corn-based ethanol, and Brazil is the dominant producer of sugarcane-based ethanol. Advances in technology and the resulting improved productivity in corn and sugarcane farming and ethanol conversion, together with biofuel policies, have contributed to the significantly expanded production of both types of ethanol in the past 20 years. These advances and improvements have helped bioethanol achieve increased energy and GHG emission benefits when compared with those of petroleum gasoline.

-Wang et al.

In the study, the team used an updated, upgraded version of the GREET model (developed at Argonne by Dr. Wang and colleagues) to estimate life-cycle energy consumption and GHG emissions for the five bioethanol production pathways on a consistent basis. The GREET model covers bioethanol production pathways extensively; the team updated key parameters in the target pathways based on recent research.

Even when they included the highly debated land-use change (LUC) GHG emissions, when the feedstock was changed from corn to sugarcane and then to cellulosic biomass, bioethanol's reductions in energy use and GHG emissions, when compared with those of gasoline, increased significantly. Thus, they concluded, in the long term, it is cellulosic ethanol production that will offer the greatest energy and GHG emission benefits.

WTW GHG emission reductions for ethanol pathways relative to gasoline. Values are reductions for P10-P90 (P50), relative to P50 of gasoline GHG.
	Corn	Sugarcane	Corn stover	Switchgrass	Miscanthus
Including LUC emissions	19-48% (34%)	40-62% (51%)	90-103% (96%)	77-97% (88%)	101-115% (108%)
Excluding LUC emissions	29-57% (44%)	66-71% (68%)	89-102% (94%)	79-98% (89%)	88-102% (95%)

They separated GHG emissions into WTP (well-to-pump); PTW (pump-to-wheel); biogenic CO₂ (i.e., carbon in bioethanol); and LUC GHG emissions. Combustion emissions are the most significant GHG emission source for all fuel pathways; however, they noted, in the five bioethanol cases, biogenic CO₂ in ethanol offsets ethanol combustion GHG emissions almost entirely.

Because of the ongoing debate about the values and associated uncertainties of LUC GHG emissions, they produced two separate sets of results for ethanol: one with LUC emissions included, and the other with LUC emissions excluded. To show the importance of key parameters affecting WTW GHG emissions results for a given fuel pathway, they conducted a sensitivity analysis of GHG emissions with GREET for all six pathways with P10 and P90 values as the minimum and maximum value for each parameter. Findings of this exercise included:

• Petroleum gasoline refining efficiency and recovery efficiency of the petroleum feedstock are the most sensitive parameters.

• For corn ethanol, the N₂O conversion rate in cornfields is the most sensitive factor, followed by the ethanol plant energy consumption. Enzyme and yeast used in the corn ethanol production process are not among the five most influential parameters in the corn ethanol life cycle.

• For sugarcane ethanol, the most significant parameters, in order of importance, are ethanol yield per unit of sugarcane, the N₂O conversion rate in sugarcane fields, nitrogen fertilizer usage intensity, sugarcane farming energy use and the mechanical harvest share. Sugarcane farming is evolving as mechanical harvesting becomes more widespread and mill by-products are applied as soil amendments.

• The three cellulosic ethanol pathways have similar results. The electricity credit is the most significant parameter (except for switchgrass ethanol, for which the N₂O conversion rate is the most significant).

• Enzyme use is a more significant factor in cellulosic ethanol pathways than in the corn ethanol pathway because the greater recalcitrance of the feedstock currently requires higher enzyme dosages in the pretreatment stage.

• The impact of fertilizer-related parameters on WTW GHG emissions results depends on the fertilizer intensity of feedstock farming.

Resources

• Michael Wang et al. (2012) Well-to-wheels energy use and greenhouse gas emissions of ethanol from corn, sugarcane and cellulosic biomass for US use. Environ. Res. Lett. 7 045905 doi: 10.1088/1748-9326/7/4/045905

http://www.greencarcongress.com/2013/01/wang-20130122.htm