Toward consistent evaluation of CO2-EOR: A meta-analysis of life cycle assessments
Bibliographic record
Abstract
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 hinder the evaluation’s reliability for policy development. To address these methodological inconsistencies, this study conducted a systematic meta-analysis of global life cycle assessment (LCA) studies examining GHG emissions from CO2-EOR systems utilizing both natural and industrial sources. The research developed and implemented a standardized methodological framework incorporating comprehensive screening, clustering, and harmonization of critical background parameters, particularly electricity grid emissions intensities. The analysis employed both economic allocation and substitution approaches to evaluate life cycle emissions across the complete supply chain. The harmonized gate-to-gate (GtG) emissions resulted in a range from 14 to 167 kgCO2e/bbl, with a median of 56 kgCO2e/bbl. Statistical analysis revealed that electricity consumption exhibited a stronger correlation with emissions than net CO2 utilization, emphasizing the importance of electricity sourcing in LCA evaluations. When expanding to well-to-wheel boundaries, the choice of allocation methodology emerged as a dominant determinant of LCA, with median GHG intensities varying from +538 kgCO2e/bbl using economic allocation to -250 kgCO2e/bbl using substitution approaches. Additionally, the CO2 source characteristics, displaced crude oil types, and EOR process design significantly influenced 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 critically impact CO2-EOR evaluations, guiding for enhancing the robustness of future assessments and developing reliable policy and research recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.143 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".