Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Brandt, Adam R., Sun, Yuchi, Bharadwaj, Sharad, Livingston, David, Tan, Eugene, Gordon, Deborah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687841/
https://www.ncbi.nlm.nih.gov/pubmed/26695068
http://dx.doi.org/10.1371/journal.pone.0144141
_version_ 1782406676527185920
author Brandt, Adam R.
Sun, Yuchi
Bharadwaj, Sharad
Livingston, David
Tan, Eugene
Gordon, Deborah
author_facet Brandt, Adam R.
Sun, Yuchi
Bharadwaj, Sharad
Livingston, David
Tan, Eugene
Gordon, Deborah
author_sort Brandt, Adam R.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4687841
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-46878412015-12-31 Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production Brandt, Adam R. Sun, Yuchi Bharadwaj, Sharad Livingston, David Tan, Eugene Gordon, Deborah PLoS One Research Article 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. Public Library of Science 2015-12-22 /pmc/articles/PMC4687841/ /pubmed/26695068 http://dx.doi.org/10.1371/journal.pone.0144141 Text en © 2015 Brandt et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Brandt, Adam R.
Sun, Yuchi
Bharadwaj, Sharad
Livingston, David
Tan, Eugene
Gordon, Deborah
Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production
title Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production
title_full Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production
title_fullStr Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production
title_full_unstemmed Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production
title_short Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production
title_sort energy return on investment (eroi) for forty global oilfields using a detailed engineering-based model of oil production
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687841/
https://www.ncbi.nlm.nih.gov/pubmed/26695068
http://dx.doi.org/10.1371/journal.pone.0144141
work_keys_str_mv AT brandtadamr energyreturnoninvestmenteroiforfortyglobaloilfieldsusingadetailedengineeringbasedmodelofoilproduction
AT sunyuchi energyreturnoninvestmenteroiforfortyglobaloilfieldsusingadetailedengineeringbasedmodelofoilproduction
AT bharadwajsharad energyreturnoninvestmenteroiforfortyglobaloilfieldsusingadetailedengineeringbasedmodelofoilproduction
AT livingstondavid energyreturnoninvestmenteroiforfortyglobaloilfieldsusingadetailedengineeringbasedmodelofoilproduction
AT taneugene energyreturnoninvestmenteroiforfortyglobaloilfieldsusingadetailedengineeringbasedmodelofoilproduction
AT gordondeborah energyreturnoninvestmenteroiforfortyglobaloilfieldsusingadetailedengineeringbasedmodelofoilproduction