OPGEE: The Oil Production Greenhouse gas Emissions Estimator
- Current research suggests that GHG emissions from petroleum production can be quite variable. Some oil production facilities can have quite low emissions if they do not rely on energy intensive production methods and implement effective controls on fugitive emissions sources. In contrast, some crude oil sources can have higher GHG emissions per unit of energy produced if they rely on energy-intensive production methods or process large volumes of fluids per unit of energy produced.
The Oil Production Greenhouse gas Emissions Estimator (OPGEE) is an engineering-based life cycle assessment (LCA) tool for the measurement of greenhouse gas (GHG) emissions from the production, processing, and transport of crude petroleum. The system boundary of OPGEE extends from initial explo- ration to the refinery entrance gate.
OPGEE is built for maximum transparency, using public data sources where possible and being implemented in a user-accessible Microsoft Excel format.
Current stable model and documentation
Model development versions [not for citation or distribution - use current version above]
See the OPGEE GitHub page for the current version of the model under development: OPGEE developer page
A service called xltrail is used to track changes to the OPGEE .xlsx binary files: OPGEE xltrail edit record
Older versions [Posted for reference only, please use current version above]
- OPGEE model v2.0c - Documentation [PDF], Model [XLSM] (Released February 13th, 2018)
- OPGEE model v2.0b - Documentation [PDF], Model [XLSM] (Released July 17th, 2017)
- OPGEE model v2.0a - Documentation [PDF], Model [XLSM] (Released March 27th, 2017)
- OPGEE embodied energy supplemental calculation [XLSM] (Released September 30, 2015)
- OPGEE model v1.1 Draft E - Documentation [PDF], Model [XLSM] (Released June 4th, 2015)
- OPGEE model v1.1 Draft D - Documentation [PDF], Model [XLSM] (Released October 10th, 2014)
- OPGEE model v1.1 Draft C - Documentation [PDF], Model [XLSM] (Released July 10th, 2014)
- OPGEE model v1.1 Draft B - Documentation [PDF], Model [XLSM] (Released March 11th, 2014)
Publications
2018
*Brandt, A.R., M.S. Masnadi, J.G. Englander, J.G. Koomey, D. Gordon. Climate-wise oil choices in a world of oil abundance. Environmental Research Letters DOI: 10.1088/1748- 9326/aaae76
*Masnadi, M.S., D. Schunack, Y. Li, S.O. Roberts, A.R. Brandt, H.M. El-Houjeiri, S. Przesmitzki, M.Q. Wang. Well-to-refinery emissions and net-energy analysis of China?s crude-oil supply. Nature Energy. DOI: 10.1038/s41560-018-0090-7
2017
Cooney, G., M. Jamieson, J. Marriott, J. Bergerson, A.R. Brandt, T.J. Skone. Updating the US life cycle GHG petroleum baseline to 2014 with projections to 2014 using open-source engineering-based models. Environmental Science & Technology DOI: 10.1021/acs.est.6b02819
Gvakharia, A., E.A. Kort, M.L. Smith, J. Peischl, J.P. Schwarz, A.R. Brandt, T.B. Ryerson, C. Sweeney. Methane, black carbon, and ethane emissions from natural gas flares in the Bakken Shale, ND. Environmental Science & Technology. DOI: 10.1021/acs.est.6b05183
Masnadi, M.S., Brandt, A.R. Climate impacts of oil extraction increase significantly with oilfield age. Nature Climate Change (2017).DOI: 10.1038/nclimate3347
Wang, J., O'Donnell, J., Brandt, A.R. Potential solar energy use in the global petroleum sector (2017) Energy, 118, pp. 884-892. DOI: 10.1016/j.energy.2016.10.107
Yeh, S., Ghandi, A., Scanlon, B.R., Brandt, A.R., Cai, H., Wang, M.Q., Vafi, K., Reedy, R.C. Energy Intensity and Greenhouse Gas Emissions from Oil Production in the Eagle Ford Shale. Energy & Fuels DOI: 10.1021/acs.energyfuels.6b02916
2016
*Brandt, A.R., T. Yeskoo, S. McNally, K. Vafi, S. Yeh, H. Cai, M.Q. Wang. Energy intensity and greenhouse gas emissions from tight oil production in the Bakken formation. Energy & Fuels. DOI: 10.1021/acs.energyfuels.6b01907
Cooney, G., M. Jamieson, J. Marriott, J. Bergerson, A.R. Brandt, T.J. Skone. Updating the US life cycle GHG petroleum baseline to 2014 with projections to 2014 using open-source engineering-based models. Environmental Science & Technology DOI: 10.1021/acs.est.6b02819
2015
Brandt, A.R. (2015) Embodied energy and GHG emissions from material use in conventional and unconventional oil and gas operations. Environmental Science & Technology. DOI:10.1021/acs.est.5b03540.
OPGEE model v1.1 Draft A - Documentation [PDF], Model [XLSM] (Released March 5th, 2013)
OPGEE model v1.0 - Documentation [PDF], Model [XLSM] (Released September 17th, 2012)
OPGEE model v1.0 Draft A - Documentation [PDF], Model [XLSX] (Released June 25th, 2012)
Site content
- Brandt, Adam, Holger Teichgraeber, Charles Kang, Charles Barnhart, Michael Carbajales-Dale, and Sgouris Sgouridis. “Blow Wind Blow: Capital Deployment in Variable Energy Systems”, Energy, 224 (June 1, 2021): 120198.
- Lyon, David, Benjamin Hmiel, Ritesh Guatam, and et. all. “Concurrent Variation in Oil and Gas Methane Emissions and Oil Price During the COVID-19 Pandemic”, Atmospheric Chemistry and Physics, 21, no. 9 (May 3, 2021): 6605–6626,. https://doi.org/10.5194/acp-21-6605-2021.
- Kang, Mary, Adam Brandt, Zhong Zheng, Jade Boutot, Chantel Yung, Adam Peltz, and Robert Jackson. “Orphaned Oil and Gas Well stimulus—Maximizing Economic and Environmental Benefits”, Science of the Anthropocene, 9, no. 1 (April 28, 2021): 00161. https://doi.org/10.1525/elementa.2020.20.00161.
- Sherwin, Evan, Yuanlei Chen, Arvind Ravikumar, and Adam Brandt. “Single-Blind Test of Airplane-Based Hyperspectral Methane Detection via Controlled Releases”, Elementa: Science of the Anthropocene, 9, no. 1 (April 24, 2021): 00063. https://doi.org/10.1525/elementa.2021.00063.
- Sleep, Sylvia, Zainab Dadashi, Yuanlei Chen, Adam Brandt, Heather MacLean, and Joule Bergerson. “Improving Robustness of LCA Results through Stakeholder Engagement: A Case Study of Emerging Oil Sands Technologies”, Journal of Cleaner Production, 281 (January 24, 2021): 125277. https://doi.org/10.1016/j.jclepro.2020.125277.
- Teichgraeber, Holger, and Adam Brandt. “Optimal Design of an Electricity-Intensive Industrial Facility Subject to Electricity Price Uncertainty: Stochastic Optimization and Scenario Reduction”, Chemical Engineering Research and Design, 163 (November 2020): 204-16. https://doi.org/10.1016/j.cherd.2020.08.022.
- Teichgraeber, Holger, Constantin Lindenmeyer, Nils Baumgärtner, Leander Kotzur, Detlef Stolten, Martin Robinius, André Bardow, and Adam Brandt. “Extreme Events in Time Series Aggregation: A Case Study for Optimal Residential Energy Supply Systems”, Applied Energy, 275 (October 1, 2020): 115223. https://doi.org/10.1016/j.apenergy.2020.115223.
- Nie, Yuhao, Yuchi Sun, Yuanlei Chen, Rachel Orsini, and Adam Brandt. “PV Power Output Prediction from Sky Images Using Convolutional Neural Network: The Comparison of Sky-Condition-Specific Sub-Models and an End-to-End Model”, Journal of Renewable and Sustainable Energy, 12, no. 4 (August 25, 2020): 046101. https://doi.org/10.1063/5.0014016.
- Nie, Yuhao, Siduo Zhang, Ryan Liu, Daniel Roda-Stuart, Arvind Ravikumar, Alex Bradley, Mohammad Masnadi, Adam Brandt, Joule Bergerson, and Xiaotao Bi. “Greenhouse-Gas Emissions of Canadian Liquefied Natural Gas for Use in China: Comparison and Synthesis of Three Independent Life Cycle Assessments”, Journal of Cleaner Production, 258 (June 10, 2020): 120701. https://doi.org/10.1016/j.jclepro.2020.120701.
- Jing, Liang, Hassan El-Houjeiri, Jean-Christophe Monfort, Adam Brandt, Mohammad Masnadi, Deborah Gordon, and Joule Bergerson. “Carbon Intensity of Global Crude Oil Refining and Mitigation Potential”, Nature Climate Change, 10 (June 2, 2020): 526–532. https://doi.org/10.1038/s41558-020-0775-3.
- Klise, Katherine, Bethany Nicholson, Carl Laird, Arvind Ravikumar, and Adam Brandt. “Sensor Placement Optimization Software Applied to Site-Scale Methane-Emissions Monitoring”, Journal of Environmental Engineering, 146, no. 7 (April 24, 2020). https://doi.org/10.1061/(ASCE)EE.1943-7870.0001737.
- Masnadi, Mohammad, Patrick Perrier, Jingfang Wang, Jeff Rutherford, and Adam Brandt. “Statistical Proxy Modeling for Life Cycle Assessment and Energetic Analysis”, Energy, 194 (April 1, 2020): 116882. https://doi.org/10.1016/j.energy.2019.116882.
- Ravikumar, Arvind, Daniel Roda-Stuart, Ryan Liu, Alexander Bradley, Joule Bergerson, Yuhao Nie, Siduo Zhang, Xiaotao Bi, and Adam Brandt. “Repeated Leak Detection and Repair Surveys Reduce Methane Emissions over Scale of Years”, Environmental Research Letters, 15, no. 3 (February 26, 2020): 034029. https://doi.org/10.1088/1748-9326/ab6ae1.
- Wang, Jingfan, Lyne Tchapmi, Arvind Ravikumar, Mike McGuire, Clay Bell, Daniel Zimmerle, Silvio Savarese, and Adam Brandt. “Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera”, Applied Energy, 257 (January 1, 2020): 113998. https://doi.org/10.1016/j.apenergy.2019.113998.
- Venugopal, Vignesh, Yuchi Sun, and Adam Brandt. “Short-Term Solar PV Forecasting Using Computer Vision: The Search for Optimal CNN Architectures for Incorporating Sky Images and PV Generation History”, Journal of Renewable and Sustainable Energy, 11 (November 12, 2019): 066102 . https://doi.org/10.1063/1.5122796.
- Levi, Patricia, Simon Kurland, Michael Carbajales-Dale, John Weyant, Adam Brandt, and Sally Benson. “Macro-Energy Systems: Toward a New Discipline”, Joule, 3, no. 10 (October 16, 2019): 2282-86. https://doi.org/10.1016/j.joule.2019.07.017.
- Ravikumar, Arvind, Sindhu Sreedhara, Jingfang Wang, Jacob Englander, Daniel Roda-Stuart, Clay Bell, Daniel Zimmerle, David Lyon, Isabel Mogstad, Ben Ratner, and Adam Brandt. “Single-Blind Inter-Comparison of Methane Detectiontechnologies – Results from the Stanford EDF MobileMonitoring Challenge”, Elementa: Science of the Anthropocene, 7, no. 37 (September 10, 2019). https://doi.org/10.1525/elementa.373.
- “Short-Term Solar Power Forecast With Deep Learning: Exploring Optimal Input and Output Configuration”, Solar Energy, 188 (August 2019): 730-41. https://doi.org/10.1016/j.solener.2019.06.041.
- Yuan, Mengyao, Holger Teichgraeber, Jennifer Wilcox, and Adam Brandt. “Design and Operations Optimization of Membrane-Based Flexible Carbon Capture”, International Journal of Greenhouse Gas Control, 84 (May 2019): 154-63. https://doi.org/10.1016/j.ijggc.2019.03.018.
- Sun, Yuchi, Vignesh Venugopal, and Adam Brandt. “Convolutional Neural Network for Short-Term Solar Panel Output Prediction”, IEEE World Conference on Photovoltaic Energy Conversion (WCPEC), 7 (November 29, 2018): 18288267. https://doi.org/10.1109/PVSC.2018.8547400.