Co-optimization of Enhanced Oil Recovery and Carbon Sequestration
Bibliographic record
Abstract
In this paper, we present what is to our knowledge the first theoretical economic analysis of CO2enhanced oil recovery (EOR).This technique, which has been used successfully in a number of oil plays (notably in West Texas, Wyoming, and Saskatchewan), entails injection of CO2 into mature oil fields in a manner that reduces the oil's viscosity, thereby enhancing the rate of extraction.As part of this process, significant quantities of CO2 remain sequestered in the reservoir.If CO2 emissions are regulated, oil producers using EOR should therefore be able to earn sequestration credits in addition to oil revenues.We develop a theoretical framework that analyzes the dynamic co-optimization of oil extraction and CO2 sequestration, through the producer's choice at each point in time of an optimal CO2 fraction in the injection stream (the control variable).We find that the optimal fraction is likely to decline monotonically over time, and reach zero before the optimal termination time.Numerical simulations, based on an ongoing EOR project in Wyoming, confirm this result.They show also that cumulative sequestration is positively related to the oil price, and is in fact much more responsive to oil-price increases than to increases in the carbon tax.Only at very high taxes does a tradeoff between oil output and sequestration arise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".