Net CO2 Stored in North American EOR Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract One promising method for reducing CO2 emissions by storing the CO2 in oil reservoirs is CO2 Enhanced Oil Recovery (EOR). In order to make a significant contribution to mitigating climate change from emissions of GHG's, CO2 EOR must actually reduce CO2 emissions by storing net positive volumes of CO2. This requires that CO2 EOR schemes store more CO2 in the subsurface than the execution of the project emits (net positive storage of CO2). Fugitive emissions associated with CO2 EOR include primarily the burning of fossil fuels (fuel gas) to power CO2 injection compressors and the on-site consumption of electric power which results in CO2 emissions off-site where the power was generated. Evaluating the effectiveness of CO2 EOR in reducing CO2 emissions must be conducted in an unbiased way where only relevant fugitive emissions that are directly connected with the CO2 EOR project are deducted. It has been suggested that fugitive emissions from downstream oil refining and consumption of the transportation products should be deducted from the net CO2 stored by CO2 EOR projects. This presumes that these emissions (refining & consumption) are incremental to the EOR project and would not occur if the EOR project was not executed. World oil production is determined by world oil demand and if CO2 EOR projects were not undertaken, some other source of oil would step forward and fill the gap. Therefore, shutting down CO2 EOR projects will not lead to a decrease in incremental refining and consumption emissions. When downstream refining and product consumption fugitive emissions are correctly excluded from the calculation of Project Life-cycle CO2 EOR storage, it is clear that CO2 EOR does result in net positive CO2 storage.
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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 it