Article pubs.acs.org/est
Reproducibility of LCA Models of Crude Oil Production Kourosh Vafi† and Adam R. Brandt*,† †
Department of Energy Resources Engineering, Stanford University, Stanford, California 94305-2220, United States S Supporting Information *
ABSTRACT: 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 rootmean-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.
2. INTRODUCTION Greenhouse gas (GHG) impacts from producing, processing, and transporting crude petroleum vary with production practices and crude oil quality, as well as with the location of production. Previous work found that energy-intensive secondary and tertiary recovery technologiessuch as water flooding or steam-based thermal enhanced oil recovery (EOR)can drive significant emissions increases.1,2 Similar impacts occur from heavy oil and bitumen production with energy intensive upgrading.3−6 The use of high emissions practices such as associated gas flaring can also result in significant impacts from both CO2 and emissions of noncombusted CH4.1,7−9 Fugitive emissions of CH4 from oil production operations vary significantly between operations, even within the same region. Fugitive releases are currently poorly understood.10−12 For these reasons, many studies have recently attempted to assess different crude oil production methods on a consistent basis. A major driver of scientific and technical efforts has been increased regulatory scrutiny of petroleum production operations. For example, controversy over regulatory approval of the Keystone XL pipeline has focused attention on GHG emissions from bitumen production operations of the Canadian oil sands. Other regulatory approaches rely on life-cycle comparison of fuel impacts, including the U.S. Federal Renewable Fuel Standard (RFS), the California Low Carbon Fuel Standard (LCFS), and the Fuel Quality Directive of the European Union (EUFQD).13−15 Ideally, regulatory efforts to reduce GHGs are based on a scientific understanding of the emissions impacts of different technologies or management options. A cornerstone of © 2014 American Chemical Society
scientific understanding is reproducible experimentation to test the results of a theory or a model against reality. A key challenge in LCA is that the scientific ideal of testing a model against reality via experimentation is impossible: the modeled quantity, in this case, emissions across a petroleum fuel life cycle, is the result of complex interactions across numerous industries and generally occurring in many countries. That is, the quantity that is desired to be reduced in the regulation is inherently unobservable.17 This then points to a fundamental need in LCA studies: model reproducibility and transparency are required to ensure that our scientific understanding improves over time. Such reproducibility and transparency are also required if we are to understand the comparability of results from various models. This paper seeks to review the suite of LCA models that are used to estimate emissions from oil and gas operations, and to perform a reproducibility analysis to determine causes of variation between various models. We use the OPGEE model (described below) to replicate the analyses of prior studies, and explore how results from the OPGEE model differ from these previous studies. These results point to methodological variation, which we outline. We conclude with a discussion of next steps and needs for future work. We know of no other comparative review of petroleum-specific LCA models. Received: Revised: Accepted: Published: 12978
April 18, 2014 August 20, 2014 October 3, 2014 October 3, 2014 dx.doi.org/10.1021/es501847p | Environ. Sci. Technol. 2014, 48, 12978−12985
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3. PRIOR MODEL DEVELOPMENT A variety of models and assessments have been developed to understand emissions from petroleum operations. For the purposes of this study, we can classify each model or analysis as one of four broad types, starting with the most broad and leading to models most specific to oil and gas production: (1) general LCA models utilizing sector-specific economic inputoutput modeling, (2) general LCA models that use a processbased approach to model activities in all economic sectors; (3) transportation fuel cycle LCA models; (4) crude-oil specific engineering-based LCA models. First, some models have been developed that use economic input-output modeling to compute emissions due to producing a good or service. For example, the EIO-LCA model of Carnegie Mellon University uses economic data to model emissions from production chains.16 Process-based LCA tools that model all economic activities can be used to estimate emissions from oil and gas production operations. These tools use databases of environmental fluxes from production of goods and services to calculate economywide fluxes associated with the production of a product. These tools can be open in design (e.g., GEMIS), or can be based on proprietary data sets (e.g., EcoInvent and GaBi).18−20 An advantage of these tools is that they model interactions between all parts of the economy; they will inherently include all indirect impacts from, for example, steel production for oil wells. However, these tools generally model processes at average conditions for country-scale or larger regions (e.g., crude oil production in the U.S.). We do not explore either of the above model types further here (we may examine them in future work). Next, LCA models that focus on transportation fuel pathways are often called “well-to-wheel” (WTW) models. These models include the GREET, GHGenius, and European Commission Joint Research Centre (JRC) WTW models in the United States, Canada, and the EU, respectively.21−23 WTW models are comprehensive, generally include various transportation fuel pathways (e.g., cellulosic ethanol), and are often publicly accessible. These models generally lack process-level detail, with technologies represented by fuel shares and efficiencies. For example, crude oil production in general WTW models is not modeled using physical properties or engineering analysis (e.g., depth of oilfield is not accounted for). Also, these models generally have more narrow boundaries than general LCA tools, possibly leading to truncation error. Lastly, a number of petroleum-specific LCA tools have been developed in recent years. These include models produced by consultancies, such as TIAX, Jacobs Consultancy, and Energy Redefined LLC.5,24−26 Some of these tools perform detailed calculations of the petroleum production process and leverage existing LCA tools (such as the GREET model) to perform life cycle computations. National Laboratories have also created LCA models by leveraging results from more general LCA tools.9 Also, the Oil Production Greenhouse gas Emissions Estimator (OPGEE) is an engineering-based, open-source model which was developed at Stanford University for the California Air Resources Board.1,27−29 These petroleum-specific models estimate crude oil emissions by modeling specific subprocesses within the oil production and processing stage (e.g., crude oil lifting or gas compression). This modeling specificity allows improved estimates of GHG emissions of oil production if underlying data are available, but their narrow
analysis boundaries makes consistent comparison with more general models challenging. OPGEE is a life cycle GHG model that estimates emissions from the well-to-refinery-entrance gate (WTR boundary). OPGEE is built with separate modules, including drilling, production, processing, and transport of crude. All sources of direct emissions are included in the model, including both combustion emissions and fugitive emissions. Indirect emissions are modeled using results from the GREET model. OPGEE uses defaults and “smart” default values to compute emissions even if input data are incomplete. OPGEE is explained in extensive technical documentation and articles.1,27−29
4. MATERIALS AND METHODS OPGEE was compared with a total of seven studies cited above. The version of OPGEE used is OPGEE version 1.1 Draft A, as released for California Air Resources Board public workshop in March 2013 (with one minor correction, see Supporting Information (SI)). OPGEE was compared with four engineering-based studies, based on three models, known henceforth as TIAX, Jacobs 2009, ER, and Jacobs 2012.5,24−26 OPGEE was compared to two WTW LCA models, GREET and GHGenius.6,21 OPGEE was also compared to a study from the National Energy Technology Laboratory (NETL)9 that used general LCA results with petroleum-specific modeling. 4.1. Qualitative Model Characteristics. First, we qualitatively characterized the studied GHG models along the following dimensions: (1) Data sources; (2) Model licensing and accessibility; (3) Calculation methods; (4) Model system/ study boundaries. The results of this qualitative comparison are presented in tabular and narrative form. 4.2. Quantitative Model Reproducibility Experiments. Next, we quantitatively study the divergence between OPGEE model results and prior study results when modeling the same oilfields. In all cases, we align regional specification as closely as possible between OPGEE and the study being reproduced (because each model differs in underlying data basis, some underlying data in each model may remain based on inputs from a particular region). 4.2.1. Comparison to Engineering-Based Models. We first compare OPGEE to four engineering-based LCA studies: Jacobs 2009, Jacobs 2012, TIAX, and ER. To compare OPGEE to engineering-based models, we perform a three-step analysis to progressively normalize OPGEE assumptions to assumptions in the comparative study. Before the three-step normalization, we adjust each study so that it represents a well-to-refinery input gate (WTR) system boundary (e.g., if refining and final fuel combustion are included in a study, they are removed before proceeding). In Step 1, key OPGEE input variables which describe reservoir, oil, and production characteristics are collected from the studies and included in OPGEE in the default user controls sheet (see SI). OPGEE default values are used for all other data. In Step 2, detailed OPGEE inputs as found in process-stage worksheets are aligned to values in the comparison study. Examples include: efficiencies of pumps, compressors, and flares. Also, the inclusion or exclusion of processing units (e.g., crude oil stabilizers) was aligned to the comparative study. In Step 3, system boundaries within the WTR boundary are aligned between the comparison study and OPGEE. For example, if activities such as drilling or land use change are not 12979
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oil production and consumption processes from well to vehicles, representative of national average crude oil consumed in 2005 analysis based on GaBi (see above) augmented with additional data and calculations
oil production processes from well to wheels. includes both conventional and unconventional sources. does not include embodied energy in materials engineering-based spreadsheet model. mixture of mechanistic and empirical methods
open literature and public data sets. all data available for examination
open literature and public data sets. GREET and GHGenius models
public data sources
proprietary data sources from industry sources; data reported on field-level basis
proprietary data sources; some data reported in produced documents
mix of proprietary and public data sources; fundamental modeling performed by PE International using GaBi
GHGenius
OPGEE
TIAX
Energy-Redefined
Jacobs Consultancy
NETL
boundaries
oil production processes from well to refinery product output. unclear inclusion of indirect sources. methods not clearly defined; mixture of mechanistic and empirical methods
simple engineering-based calculations based on public data
iterative calculation of all inputs for a given pathway, determined by interactions between pathways iterative calculation of all inputs for a given pathway, determined by interactions between pathways engineering-based, mixture of mechanistic and empirical
freely available to download and modify freely available to download and modify freely available to download and modify model not available. reports available to public model not available. reports available to public model not available. reports available to public model not available. reports available to public open literature and public data sets. all data available for examination
data
licensing and availability
calculations 12980
model
Table 1. Comparison of Models along Four Dimensions of Variability
5. RESULTS AND DISCUSSION 5.1. Qualitative Model Characteristics. A table outlining qualitative model characteristics is given in Table 1. A detailed methodological review of the engineering-based models is given in Table 2. Data availability is a concern for all models. Operating companies can account for fuels combusted and the electricity consumed at a facility to estimate emissions. All other tools or methods face at least some shortcomings in data. Confidential data and proprietary models are therefore often used in the current GHG literature from both academic and consultancies sources.25,29−31 Models that rely on open data sources, such as OPGEE, are challenged due to reliability and incompleteness of the data sets. OPGEE does not completely overcome these challenges, but does offer a comprehensive set of defaults and “smart defaults” that allow it to make computations even in the absence of complete information. Model accessibility varies. Some models are free to download, modify and utilize. These include governmentfunded WTW models (e.g., GREET, GHGenius) and OPGEE. Some models are not accessible, although reports produced
GREET
included in the comparative study boundary, they are deducted from OPGEE result. Our method did not attempt to recreate results from other studies by making further assumptions, ad-hoc adjustments, or obtaining missing input variables from literature. For example: for unspecified inputs, we assume OPGEE model defaults, even if closer agreement between OPGEE and the comparative model could be obtained by assuming an alternative configuration or by collecting additional data from the literature. One exception to this principle was required: if physical laws−such as conservation of mass in the gas processing system−are violated by input data from a study, the data are adjusted using OPGEE algorithmic adjustment.28 Detailed information on the collected input variables from the studies, as well as methods implementation, are given in SI. 4.2.2. Comparison of OPGEE to Well-to-Wheel Transport LCA Models. We compare OPGEE to GREET and GHGenius results for continental United States conventional crude oil production. We model crude oil production in the continental U.S. using OPGEE regional defaults for the US. For the GREET model comparison, GREET v.1_2012rev2 was used. For the GHGenius model comparison, GHGenius version 4.03 is used. In all cases, the functional unit is 1 MJ of crude oil delivered to the refinery input gate. 4.2.3. Comparison of OPGEE to NETL Model. OPGEE is populated with data inputs for various countries from the NETL 2008 report, including: crude oil, natural gas, and NGL produced; heating values of the products; production well diameter; rate of flaring and vents (mass of gas per mass of total hydrocarbon produced). Unlike other WTR studies, the OPGEE comparison to NETL does not include crude oil transportation emissions, so the functional unit is 1 MJ of crude oil produced and processed for transport. 4.3. Mathematical Indicators of Model Agreement. Using the results from the quantitative model reproducibility analysis, we then generate indicators of model agreement. We report root-mean-square error (RMSE) between OPGEE and the comparison model for each case. These RMSE values give a summary quantitative indication of the degree of divergence between each other model and OPGEE.
all transportation fuel pathways, from well to wheels. other energy industries included for completeness (e.g., coal production for power generation). limited inclusion of material and service processes all transportation fuel pathways from well to wheels; other energy industries included for completeness (e.g., coal production for power generation). limited inclusion of material and service processes oil production process from well-to-refinery gate. includes energy consumption and fugitive emissions. does not include embodied emissions in materials; includes upstream emissions associated with energy products full life cycle emissions, with focus on upstream sources. exact boundaries for upstream analysis unclear
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Table 2. Methodological Comparison between Process-Based (Engineering) LCA Models of Crude Oil Production OPGEEa
Jacobs (2009, 2012)
TIAX (2009)
included
included
not included
electricity supply
physical model of lifting accounting for energy losses during lifting (head, friction, etc.) imported natural gas-based power
physical model of lifting accounting for energy losses during lifting (head, friction, etc.) onsite generation using gas
gas processing gas reinjection
includes AGR, dehydration, demethanizer defaults to 0% reinjection
does not include demethanizer included by default, unknown percentage
heaters and drivers land use water reinjection
gas powered
electrical
included does not include thermal deaeration; does include hydrostatic head. includes frictional losses
not included includes thermal deaeration; unclear treatment of hydrostatic head. includes frictional losses
average intensity of fluid lifting multiplied by volume. nonphysical model. offsite generation using regional grid fuel mix gas processing not included gas compression modeled with very low energy intensity heaters not included, electrical compressors not included average intensity of water reinjected multiplied by volume. nonphysical model
crude processing and stabilization crude production
using these models are often available (e.g., Jacobs Consultancy). Methods of modeling crude production vary between models. WTW models generally use simple efficiencies for the production process with fuel shares (e.g., fraction of energy provided by natural gas or electricity). Other methods tabulate the energy intensity (EI) of broad production processes (e.g., fluid lifting) and calculate energy demand by multiplying EI by an activity rate (e.g., bbls of fluid lifted). Some methods use engineering fundamentals (e.g., gravitational head and friction loss in fluid lifting) to compute energy requirements. This approach promises improved accuracy but is challenged by the complexity of the underlying physical models and the requirement of field-specific data to develop and verify models. A combination of approaches is therefore commonly used. Lastly, model system boundaries differ between models. Here we make a distinction between emissions that occur directly at the site of crude production, processing, or transport (which we call “direct” emissions) and emissions that occur at other locations due to the act of producing crude oil (which we call “indirect” emissions). Fugitive emissions from oilfield tanks are an example of a direct emissions source, while example indirect emissions sources are offsite power plant emissions due to supplying electricity to the oilfield. Models that include more indirect activities within the analysis boundary obviously model a broader suite of impacts. Broader boundaries do not guarantee accuracy of model estimates, but their use reduces model bias resulting from ignoring emissions sources (known as “truncation error”). For a variety of practical reasons, increased breadth is often accompanied by reduced fidelity and model specificity, although this need not be true in all cases. 5.2. Quantitative Model Reproducibility Experiments. Figure 1 presents 12 parity charts that outline OPGEE results as compared to results from the engineering-based studies outlined above. Other model results are presented on the x-axis, while OPGEE results are presented on the y-axis. Outputs that plot near the 1:1 line (45-degree line) show agreement between OPGEE and the comparative model. Outputs that plot above the 1:1 line indicate OPGEE estimates greater than the comparative model, and vice versa. Dotted lines indicate ratios of 1:2 and 2:1 ratios between model results. To better understand the reasons behind the incongruities, WTR emissions for each comparative model are broken down into VFF (venting, flaring, and fugitive) emissions and non-VFF sources. Total WTR emissions are presented in the right-hand column of Figure 1 (c, f, i, l).
Methodological review shows that the Jacobs (2009 and 2012) model is similar to OPGEE in design. See Table 2, which notes differences between reviewed process-based engineering models. Figures 1(a) to 1(c) compare emissions intensity from OPGEE against Jacobs 2009 model. For most crudes, non-VFF emissions (Figure 1(a)) contribute to the incongruities found between Jacobs 2009 and OPGEE. Notably, the Jacobs 2009 prediction for Mars oil is significantly higher than OPGEE’s prediction. As this field is deep and has high water production rates, we hypothesize that this difference may be due to the water handling differences noted in Table 2 (deaeration and hydrostatic pressure). Since the Jacobs model is not available for comparison, we cannot determine with certainty the cause of such differences. Methodological review shows that TIAX and OPGEE are structured quite differently (see Table 2). Figure 1(f) compares WTR emission intensity from TIAX to OPGEE. In general, TIAX WTR emissions are lower than OPGEE emissions. After alignment Step 3, OPGEE and TIAX estimates become reasonably consistent. Some remaining disagreement may be due to the lack of emissions from surface processing of crude oil and gas in TIAX. Non-VFF emissions in Alaska North Slope deviate between OPGEE and TIAX due partly to significant differences in gas compression modeling, which is a large driver of emissions in ANS crude. WTI and Saudi Medium crude have slightly negative WTR emissions due to significant coproduction of natural gas and low energy requirements associated with lifting the crude. ER provides less methodological description than Jacobs or TIAX. Methodological review suggests that the ER model is a semiempirical model that performs field-level energy consumption computations. More detailed methods comparison is not possible. A major difference between OPGEE and ER is that ER analysis boundary is from the well to the refinery output gate. The OPGEE analysis boundary ends at the refinery input gate. Therefore, before the 3-stage process alignment, ER results were adjusted to a WTR system boundary (using reported ER refining intensities to subtract that portion of the life cycle). Figure 1(i) compares WTR estimates of ER against OPGEE. For most cases, OPGEE has higher WTR emissions than ER. Figure 1(g) shows that in general OPGEE non-VFF emissions are higher than ER. OPGEE non-VFF emissions from Duri crude oil are significantly higher than the ER prediction. The known method of recovery in the Duri field is steam flooding.25 The OPGEE default steam oil ratio (SOR) of 3.0 bbl/bbl is 12981
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Figure 1. Parity charts comparing OPGEE to four engineering-based, bottom-up models of oil production. Units for all axes: well to refinery gate GHG emissions (gCO2 eq/MJ LHV crude oil) VFF = Venting, flaring and fugitive emissions. WTR = Well-to-refinery entrance gate. Shape key (dotted, solid, filled) refers to degree of alignment of inputs between models.
used. Figure 1(h) shows that ER VFF emissions agree with OPGEE in some cases but not all. There is no indication of a particular pattern. Some divergence is explicable given available data. For example, Kupal ER emissions are much higher than OPGEE due to large difference in estimates of VFF emissions. It is not clear whether ER considers unconventional method of recovery for the case of Dacion. Because of this, we created two OPGEE cases for Dacion, using steam flooding and the default method of conventional oil recovery (Dacion A and Dacion B, respectively).
Methodological review shows that Jacobs 2012 is very similar to Jacobs 2009 (see Table 2). Figure 1(j), 1(k), and 1(l) compare emission intensities estimated by Jacobs 2012 against OPGEE. In most cases, non-VFF emissions calculated by Jacobs 2012 are higher than OPGEE estimates (see reasons above). VFF emissions from OPGEE are in most cases higher than Jacobs, which lessens overall differences in WTR emission intensity (see SI). Our summary judgment is that the Jacobs model results are similar to OPGEE, although some differences remain and a lack 12982
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Table 3. Root Mean Square Error (RMSE) between OPGEE and Comparative Studies Results [gCO2eq./MJ LHV]a crudes studied
a
Jacobs 2009
TIAX
ER
Jacobs 2012
GREET
GHGenius
8
9
11
11
1
1
Step1 RMSE
WTR - VFF - non-VFF
2.35 0.81 2.95
8.01 3.86 4.46
8.04 6.77 7.24
1.18 1.29 0.70
1.2 2 0.8
5.3 3.3 2
Step 2 RMSE
WTR - VFF - non-VFF
2.11 0.81 2.33
4.10 1.84 3.19
7.89 6.90 7.24
1.71 0.84 1.22
NA NA NA
NA NA NA
Step 3 RMSE
WTR - VFF - non-VFF
2.47 0.81 3.13
2.46 1.64 2.04
7.26 6.90 6.53
0.93 0.84 0.91
NA NA NA
NA NA NA
Number of crudes compared listed as “crudes studied”. WTR = Well-to-refinery inlet gate; VFF = venting, flaring and fugitives.
of transparency in the model prevents complete understanding of the differences. It is known that TIAX neglects processes (e.g., surface processing of crude oil and gas) and models other aspects very simply compared to OPGEE and Jacobs (e.g., lifting and injection of fluids modeled in a nonmechanistic manner). Little can be said about ER’s methods. Results from the comparison of OPGEE to WTW models GREET and GHGenius are presented in Table 3. As can be seen, results from GREET and OPGEE are in approximate agreement. This is not surprising, as OPGEE emissions factors and OPGEE transportation module are based on GREET computations. GHGenius has significantly higher emissions for conventional US crude production than GREET or OPGEE. Higher emissions in GHGenuis can chiefly be explained by two factors. First, GHGenius venting, fugitive, and flaring emissions are higher than those in OPGEE. U.S. venting and fugitive emissions in GHGenius equal 0.15 gCH4/MJ of crude, about 3 times larger than OPGEE emissions of 0.045 gCH4/MJ crude. Flaring rates in GHGenius for U.S. petroleum production 13.2 m3/tonne, also higher than OPGEE US default rates of 3.3 m3/tonne. GHGenius gathers information on crude oil properties from many sources, with key sources being U.S. EIA data sets and other government agencies. See chapter Twenty-seven of GHGenius documentation.21 These emissions sources have different uncertainty profiles: OPGEE flaring rates are based on empirical satellite measurements,7 while fugitive and venting emissions are based on survey data and emissions factors.32,33 Given the significant current uncertainty surrounding fugitive emissions from oil and gas systems, it is unclear how accurate fugitive emissions estimates are. Second, GHGenius energy consumption for crude production is somewhat larger than OPGEE or GREET. OPGEE default U.S. crude production and processing intensity is ∼0.03 MJ/MJ crude output from the facility. This can be compared to GHGenius crude production intensity of ∼0.05 MJ/MJ crude and GREET crude production intensity of ∼0.02 MJ/MJ crude. OPGEE finds similar production energy intensity as GHGenius in cases where production is from depleted oil fields.1 Using a nondefault setting in OPGEE with a high water−oil-ratio (WOR), for example, would align OPGEE emissions with GHGenius emissions. Figure 2 compares the estimated GHG emissions from extraction of crude oil by OPGEE against NETL results. Emissions from transportation of crude oil are excluded from both OPGEE and NETL results. In Figure 2, available data
Figure 2. Parity chart comparing OPGEE to results from NETL life cycle model, assuming that hydrocarbons are defined as crude oil and gas products (gas + NGL). In SI, alternative chart is created that assumes that hydrocarbons are equal to only crude oil. The use of the term hydrocarbons is unclear in the NETL report.
were used to convert the rate of flaring and venting from mass of gas flared/vented per mass of hydrocarbon produced to mass of gas flared/vented per mass of crude oil produced. As can be seen, in some cases, this apportionment is problematic: OPGEE emissions estimates for Algerian crude oil are significantly higher than NETL results. In SI Figure S18, results are shown where the mass of HC reported by NETL is assumed equal to mass of the crude oil produced. This causes OPGEE estimates for Algeria to align closely to NETL estimates. A possible explanation is that NETL data for Algeria imply a crude oil mass fraction of ∼33% of the total hydrocarbon produced (compared to more typical 80+% petroleum). Overall, OPGEE emissions estimates are generally congruent with NETL, but are systematically slightly higher. Table 4 shows root-mean-square errors (RMSE) between OPGEE results and comparative model results for the same fields (in gCO2eq/MJ LHV of crude at the refinery inlet gate). The RMSE generally declines slightly with alignment of model assumptions (compare Step 1 to Step 3 for same study). We note that RMSE for the two most closely aligned models, Jacobs and OPGEE, is of order 1−2 gCO2eq/MJ, or ∼5−20% of typical WTR emissions estimates. RMSE for Jacobs 2009 actually increases when assumptions are aligned, in contrast to other studied models. RMSE for TIAX and ER are substantially 12983
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for modeling emissions from a particular oilfield or group of oilfields. We recommend the use of GREET or GHGenius for comparison of crude oil to other fuel options (e.g., electricity or biofuels). For studies of the interaction of a crude production process with all economic activities, a broader process based model such GEMIS or EcoInvent (not studied in detail here) is likely to be most effective. A key motivation for OPGEE development is that it be transparent and reproducible. Transparency enables more rapid progress in understanding. Reproducibility engenders trust in results. We note that a proprietary model does not imply inaccuracy in model results. Nor does a transparent model based on public data always provide accurate estimates of emissions. However, transparency and reproducibility allows a process of review and critique that has been widely useful in other domains. In general, we believe the field of crude oil LCA modeling can make significant improvements in both of these areas.
Table 4. Emissions from OPGEE, GREET, and GHGenius in Modeling U.S. Average Crude Oil Emissions extraction leaks and flares transport total
OPGEEa
GREETb
GHGeniusc
2.9 1.3 0.7 4.9
2.3 3.3 0.5 6.0
4.5 4.6 1.1 10.3
a
U.S. conventional crude oil production modeled as follows: location changed to “Continental U.S.”, and crude shipment method changed to 1500 miles pipeline. All other settings remain at OPGEE defaults. b Crude location changed to 100% continental United States. CH4 from gas leaks and flaring from cell “Petroleum” C109 is assumed to come from upstream operations (model documentation slightly unclear). cIn order to model U.S. crude oil production the following changes were made: (1) location changed to U.S. on inputs sheet; (2) fractional tonnage of U.S. crude production changed to 100% conventional (“Crude Production” B27-G27); (3) source of Western, Central, Eastern, U.S. crude oil changed to 100% U.S.; (4) source of heavy and light refined products changed to 99.9% U.S. crude and 0.1% Mexico (this prevents division by zero error in model, as noted in Pers. comm. with D. O’Connor).
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ASSOCIATED CONTENT
S Supporting Information *
Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.
higher, although model alignment reduces RMSE for TIAX by ∼70% after Step 3. Estimating GHG emissions from oil operations is ideally a scientific undertaking. Scientific models can be either mechanistic or empirical, and most modelsincluding OPGEErely on both approaches. Given the complexity of LCA modeling, as well sparse data sets, some judgment is required in making reasonable assumptions in any crude oil LCA model. Are the results from these models comparable? In one sense, they are, because all reviewed models can be used to compute life cycle GHG emissions from oil production, processing and transport. However, each model uses different methodologies, and modeling philosophy and scope can vary between models. Despite challenges in obtaining complete congruence between models, we find the comparison of their methods instructive. For example, review of other modeling methods has pointed to productive changes that can be made to OPGEE to improve its accuracy (e.g., include deaeration of injected water). This reproducibility experiment offers value to authors of the nonopen models, who might find specific improvements for improving their models or also to make suggestions to improve OPGEE. Causes of divergence between model results varied. Differences were found to derive from • Differences in values for input variables (e.g., field depth, flaring rate); • Differences in modeling approach (mechanistic vs empirical vs simple factor multiplication); • Differences in system boundaries (e.g., economy-wide vs transport fuel WTW pathway vs on site emissions) • Differences in region modeled and regional aggregation (e.g., more generic models may aggregate data differently than OPGEE generic model) Model usefulness depends on the desired application. Models that are more sophisticated tend to be more narrow (e.g., Jacobs), while models with broad coverage of processes across different fuel pathways (e.g., GREET) tend to have simplified representations of processes. Based on the model differences noted above, we can make preliminary recommendations for model use. We recommend the use of OPGEE
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We acknowledge technical assistance from A. Burnham (Argonne National Laboratory), J. Duffy (California Air Resources Board), D. Gordon (Carnegie Endowment for Global Peace), C. Malins (International Council on Clean Transportation), D. O’Connor ((S&T)2 consultants), G. Howorth (Energy Redefined, LLC), J. Rosenfeld (ICF International), and M. Wang (Argonne National Laboratory). Original funding for OPGEE was provided by the California Air Resources Board. The authors acknowledge financial support from Stanford University School of Earth Sciences (faculty support funds) and the Carnegie Endowment for Global Peace.
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