Increasing Oil Recovery from Unconventional Shale Reservoirs Using Cyclic Carbon Dioxide Injection
Bibliographic record
Abstract
Abstract Unconventional shale reservoirs have become and large unconventional supplier of oil and gas especially in North America. They are usually produced from using hydraulic fracturing which produces and average of 7-10% per well. This research studies the application of carbon dioxide (CO2) enhanced oil recovery (EOR) in shale reservoirs to increase oil recovery to more than 20%. Cyclic CO2 injection was used to conduct all experiments rather than flooding. The main difference between both procedures and the advantage of cyclic injection over flooding in shale reservoirs is explained. A specially designed vessel was constructed and used to mimic the cyclic CO2 injection procedure. The effect of CO2 soaking pressure, CO2 soaking time, and number of soaking cycles on oil recovery was investigated. Results showed that cyclic CO2 injection can increase oil recovery substantially, however there are some points that must be taken into consideration including optimum soaking pressure and time in order to avoid a waste of time and capital with no significant increase in oil recovery. This research not only provides an experimentally backed conclusion on the ability of cyclic CO2 injection to increase oil recovery from shale reservoirs, it also points to some major issue that should be considered when applying this EOR method in unconventional shale in order to optimize the overall procedure.
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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 | 0.000 | 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) | 0.001 | 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".