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Li L, Yang J, Cao Y, Wu J. Estimates of carbon dioxide emissions based on incomplete condition information: a case study of liquefied natural gas in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:8847-8861. [PMID: 30715711 DOI: 10.1007/s11356-019-04391-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 01/25/2019] [Indexed: 06/09/2023]
Abstract
Recent calculations of carbon dioxide (CO2) emissions have faced challenges because data consist of only partial information, which is called "incomplete information." According to the emission factor method, energy consumption and CO2 emission factors with incomplete information may lead to unmatched multiplication between themselves, which affects accuracy and increases uncertainties in emission results. To address a specific case of incomplete information that has not been fully explored, we studied the effects of incomplete condition information on the estimates of CO2 emissions from liquefied natural gas (LNG) in China. Based on Chinese LNG sampling data, we obtained the specific-country CO2 emission factor for LNG in China and calculated the corresponding CO2 emissions. By applying hypothesis testing, regression analysis, variance analysis, or Monte Carlo (MC) simulations, the effects of incomplete information on the uncertainty of CO2 emission calculations in three cases were analyzed. The results indicate that calorific values have more than a 9.8% impact on CO2 emission factors and CO2 emissions with incomplete sample information. Regarding incomplete statistical information, the impact of statistical temperature on CO2 emissions exceeds 5.5%. Regarding incomplete sample and statistical information, sample and statistical temperatures can individually increase estimate biases by more than 5.2%. Significantly, the impacts of sample temperature and statistical temperature may offset each other. Therefore, the incomplete condition information is quite important and cannot be ignored in the estimation of CO2 emissions from LNG and international fair comparison.
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Affiliation(s)
- Lingyue Li
- Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, Shandong, People's Republic of China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Jing Yang
- Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, Shandong, People's Republic of China
| | - Yan Cao
- Institute for Combustion Science and Environmental Technology, Department of Chemistry, Western Kentucky University, Bowling Green, KY, 42101, USA
| | - Jinhu Wu
- Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, Shandong, People's Republic of China.
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Orellana A, Laurenzi IJ, MacLean HL, Bergerson JA. Statistically Enhanced Model of In Situ Oil Sands Extraction Operations: An Evaluation of Variability in Greenhouse Gas Emissions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:947-954. [PMID: 29232120 DOI: 10.1021/acs.est.7b04498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Greenhouse gas (GHG) emissions associated with extraction of bitumen from oil sands can vary from project to project and over time. However, the nature and magnitude of this variability have yet to be incorporated into life cycle studies. We present a statistically enhanced life cycle based model (GHOST-SE) for assessing variability of GHG emissions associated with the extraction of bitumen using in situ techniques in Alberta, Canada. It employs publicly available, company-reported operating data, facilitating assessment of inter- and intraproject variability as well as the time evolution of GHG emissions from commercial in situ oil sands projects. We estimate the median GHG emissions associated with bitumen production via cyclic steam stimulation (CSS) to be 77 kg CO2eq/bbl bitumen (80% CI: 61-109 kg CO2eq/bbl), and via steam assisted gravity drainage (SAGD) to be 68 kg CO2eq/bbl bitumen (80% CI: 49-102 kg CO2eq/bbl). We also show that the median emissions intensity of Alberta's CSS and SAGD projects have been relatively stable from 2000 to 2013, despite greater than 6-fold growth in production. Variability between projects is the single largest source of variability (driven in part by reservoir characteristics) but intraproject variability (e.g., startups, interruptions), is also important and must be considered in order to inform research or policy priorities.
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Affiliation(s)
- Andrea Orellana
- Department of Chemical and Petroleum Engineering, University of Calgary , 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Ian J Laurenzi
- ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale, New Jersey 08801-3059, United States
| | - Heather L MacLean
- Departments of Civil Engineering, Chemical Engineering and Applied Chemistry, School of Public Policy and Governance, University of Toronto , Toronto, Ontario Canada M5S 1A4
| | - Joule A Bergerson
- Department of Chemical and Petroleum Engineering, University of Calgary , 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
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Estimating decades-long trends in petroleum field energy return on investment (EROI) with an engineering-based model. PLoS One 2017; 12:e0171083. [PMID: 28178318 PMCID: PMC5298284 DOI: 10.1371/journal.pone.0171083] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 01/16/2017] [Indexed: 11/19/2022] Open
Abstract
This paper estimates changes in the energy return on investment (EROI) for five large petroleum fields over time using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE). The modeled fields include Cantarell (Mexico), Forties (U.K.), Midway-Sunset (U.S.), Prudhoe Bay (U.S.), and Wilmington (U.S.). Data on field properties and production/processing parameters were obtained from a combination of government and technical literature sources. Key areas of uncertainty include details of the oil and gas surface processing schemes. We aim to explore how long-term trends in depletion at major petroleum fields change the effective energetic productivity of petroleum extraction. Four EROI ratios are estimated for each field as follows: The net energy ratio (NER) and external energy ratio (EER) are calculated, each using two measures of energy outputs, (1) oil-only and (2) all energy outputs. In all cases, engineering estimates of inputs are used rather than expenditure-based estimates (including off-site indirect energy use and embodied energy). All fields display significant declines in NER over the modeling period driven by a combination of (1) reduced petroleum production and (2) increased energy expenditures on recovery methods such as the injection of water, steam, or gas. The fields studied had NER reductions ranging from 46% to 88% over the modeling periods (accounting for all energy outputs). The reasons for declines in EROI differ by field. Midway-Sunset experienced a 5-fold increase in steam injected per barrel of oil produced. In contrast, Prudhoe Bay has experienced nearly a 30-fold increase in amount of gas processed and reinjected per unit of oil produced. In contrast, EER estimates are subject to greater variability and uncertainty due to the relatively small magnitude of external energy investments in most cases.
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Cooney G, Jamieson M, Marriott J, Bergerson J, Brandt A, Skone TJ. Updating the U.S. Life Cycle GHG Petroleum Baseline to 2014 with Projections to 2040 Using Open-Source Engineering-Based Models. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2017; 51:977-987. [PMID: 28092937 DOI: 10.1021/acs.est.6b02819] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The National Energy Technology Laboratory produced a well-to-wheels (WTW) life cycle greenhouse gas analysis of petroleum-based fuels consumed in the U.S. in 2005, known as the NETL 2005 Petroleum Baseline. This study uses a set of engineering-based, open-source models combined with publicly available data to calculate baseline results for 2014. An increase between the 2005 baseline and the 2014 results presented here (e.g., 92.4 vs 96.2 g CO2e/MJ gasoline, + 4.1%) are due to changes both in modeling platform and in the U.S. petroleum sector. An updated result for 2005 was calculated to minimize the effect of the change in modeling platform, and emissions for gasoline in 2014 were about 2% lower than in 2005 (98.1 vs 96.2 g CO2e/MJ gasoline). The same methods were utilized to forecast emissions from fuels out to 2040, indicating maximum changes from the 2014 gasoline result between +2.1% and -1.4%. The changing baseline values lead to potential compliance challenges with frameworks such as the Energy Independence and Security Act (EISA) Section 526, which states that Federal agencies should not purchase alternative fuels unless their life cycle GHG emissions are less than those of conventionally produced, petroleum-derived fuels.
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Affiliation(s)
- Gregory Cooney
- National Energy Technology Laboratory , 626 Cochrans Mill Road, P.O. Box 10940, Pittsburgh, Pennsylvania 15236, United States
| | - Matthew Jamieson
- National Energy Technology Laboratory , 626 Cochrans Mill Road, P.O. Box 10940, Pittsburgh, Pennsylvania 15236, United States
| | - Joe Marriott
- National Energy Technology Laboratory , 626 Cochrans Mill Road, P.O. Box 10940, Pittsburgh, Pennsylvania 15236, United States
| | - Joule Bergerson
- University of Calgary EEEL Building University of Calgary , 2500 University Drive NW, Calgary, Alberta Canada T2N 1N4
| | - Adam Brandt
- Stanford University , 066 Green Earth Sciences Building, 367 Panama St., Stanford, California 94305, United States
| | - Timothy J Skone
- National Energy Technology Laboratory , 626 Cochrans Mill Road, P.O. Box 10940, Pittsburgh, Pennsylvania 15236, United States
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Vafi K, Brandt A. GHGfrack: An Open-Source Model for Estimating Greenhouse Gas Emissions from Combustion of Fuel during Drilling and Hydraulic Fracturing. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:7913-7920. [PMID: 27341087 DOI: 10.1021/acs.est.6b01940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper introduces GHGfrack, an open-source engineering-based model that estimates energy consumption and associated GHG emissions from drilling and hydraulic fracturing operations. We describe verification and calibration of GHGfrack against field data for energy and fuel consumption. We run GHGfrack using data from 6927 wells in Eagle Ford and 4431 wells in Bakken oil fields. The average estimated energy consumption in Eagle Ford wells using lateral hole diameters of 8 (3)/4 and 6 (1)/8 in. are 2.25 and 2.73 TJ/well, respectively. The average estimated energy consumption in Bakken wells using hole diameters of 6 in. for horizontal section is 2.16 TJ/well. We estimate average greenhouse gas (GHG) emissions of 419 and 510 tonne of equivalent CO2 per well (tonne of CO2 eq/well) for the two aforementioned assumed geometries in Eagle Ford, respectively, and 417 tonne of CO2 eq/well for the case of Bakken. These estimates are limited only to GHG emissions from combustion of diesel fuel to supply energy only for rotation of drill string, drilling mud circulation, and fracturing pumps. Sensitivity analysis of the model shows that the top three key variables in driving energy intensity in drilling are the lateral hole diameter, drill pipe internal diameter, and mud flow rate. In hydraulic fracturing, the top three are lateral casing diameter, fracturing fluid volume, and length of the lateral.
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Affiliation(s)
- Kourosh Vafi
- Department of Energy Resources Engineering, Stanford University , Stanford, California 94305, United States
| | - Adam Brandt
- Department of Energy Resources Engineering, Stanford University , Stanford, California 94305, United States
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Brandt AR, Sun Y, Bharadwaj S, Livingston D, Tan E, Gordon D. Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production. PLoS One 2015; 10:e0144141. [PMID: 26695068 PMCID: PMC4687841 DOI: 10.1371/journal.pone.0144141] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Accepted: 10/21/2015] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
- Adam R. Brandt
- Department of Energy Resources Engineering, Stanford University, 367 Panama St., Stanford, CA 94035, United States of America
- * E-mail:
| | - Yuchi Sun
- Department of Energy Resources Engineering, Stanford University, 367 Panama St., Stanford, CA 94035, United States of America
| | - Sharad Bharadwaj
- Department of Energy Resources Engineering, Stanford University, 367 Panama St., Stanford, CA 94035, United States of America
| | - David Livingston
- Carnegie Endowment for International Peace, 1779 Massachusetts Ave. NW, Washington, DC 20036, United States of America
| | - Eugene Tan
- Carnegie Endowment for International Peace, 1779 Massachusetts Ave. NW, Washington, DC 20036, United States of America
| | - Deborah Gordon
- Carnegie Endowment for International Peace, 1779 Massachusetts Ave. NW, Washington, DC 20036, United States of America
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Brandt AR. Embodied Energy and GHG Emissions from Material Use in Conventional and Unconventional Oil and Gas Operations. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:13059-13066. [PMID: 26421352 DOI: 10.1021/acs.est.5b03540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
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.
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Affiliation(s)
- Adam R Brandt
- Department of Energy Resources Engineering, Stanford University , Stanford, California 94305, United States
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Brandt AR, Sun Y, Vafi K. Uncertainty in regional-average petroleum GHG intensities: countering information gaps with targeted data gathering. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:679-686. [PMID: 25517046 DOI: 10.1021/es505376t] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
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.
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Affiliation(s)
- Adam R Brandt
- Department of Energy Resources Engineering, Stanford University , Stanford, California 94305, United States
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