Gate-to-gate life-cycle assessment of immiscible CO2-EOR operation in heavy oil using real operation data
Bibliographic record
Abstract
The urgent need to reduce greenhouse gas emissions while meeting global energy demands requires innovative approaches in oil production methods. While CO 2 Enhanced Oil Recovery (CO 2 -EOR) shows promise, comprehensive life-cycle assessment (LCA) of its environmental impact and its dynamics in heavy oil fields remain limited. This study uses operation data from the Mervin oil field in Saskatchewan, Canada, to evaluate the LCA of CO 2 -EOR in a heavy oil reservoir. The Oil Production Greenhouse Gas Emissions Estimator (OPGEE) enables analysis of gate-to-gate carbon intensity and CO 2 sequestration effectiveness in immiscible CO 2 huff-and-puff operations. This work demonstrates that the studied CO 2 huff-and-puff method for heavy oil EOR can achieve an average negative gate-to-gate carbon intensity of -1.99 gCO 2 eq./MJ considering full CO 2 sequestration credit. This method reduces carbon intensity by 156% compared to traditional thermal EOR techniques, even without carbon credits. Emission sources include venting, burner, and vaporizer operations, contributing 68% of total emissions. The CO 2 sequestration ratio decreases from 60% in first three years to 17% later, reflecting operational changes and varying fresh liquid CO 2 injection ratios. This work provides useful insights for implementing and optimizing CO 2 -EOR systems in shallow heavy oil fields worldwide, contributing to less carbon intensive oil production practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".