Toward consistent evaluation of CO2-EOR: A meta-analysis of life cycle assessments

Over the last two decades, extensive research on the life cycle carbon footprints of CO₂-enhanced oil recovery (EOR) has yielded a wide range of results, yet inconsistent methodologies have hindered the reliability of these evaluations for policy development. To address these methodological inconsistencies, this study conducted a systematic meta-analysis of global life cycle assessment (LCA) studies examining GHG emission factors from CO₂-EOR systems utilizing both natural and industrial sources. The research developed and implemented a standardized evaluation workflow incorporating comprehensive screening, eligibility assessment, inclusion/exclusion criteria, data validation, clustering, and harmonization of critical background parameters, particularly electricity grid emission factors. The analysis employed both economic allocation and substitution approaches to evaluate life cycle emission factors across the complete supply chain. The harmonized gate-to-gate (GtG) emission factors resulted in a range from 14 to 167 kg CO₂e/bbl, with a median of 56 kg CO₂e/bbl. Statistical analysis revealed that electricity consumption exhibited a stronger correlation with emission factors than net CO₂ utilization, emphasizing the importance of electricity sourcing in LCA evaluations. When expanding to cradle-to-grave boundaries, the choice of allocation methodology emerged as a dominant driver of LCA, with median GHG emission factors varying from +538 kg CO₂e/bbl using economic allocation to -250 kg CO₂e/bbl using substitution approaches. Additionally, the CO₂ source characteristics, the type of displaced crude oil, and the EOR process design were found to significantly influence results. This systematic assessment underscores the imperative for standardized monitoring and comprehensive reporting of venting and fugitive emissions to reduce LCA uncertainties. The findings demonstrate how methodological choices, boundary definitions, and underlying assumptions critically impact CO₂-EOR emission factor evaluations, providing guidance for enhancing the robustness of future LCAs and informing reliable policy and research recommendations.