Bibliographic record
Abstract
Enhanced oil recovery (EOR) techniques are used to increase both the amount of oil recovered and the rate at which it is recovered. EOR techniques include thermal recovery processes (steam injection and in situ combustion), gas injection (carbon dioxide injection), chemical injection (polymer injection) and microbial enhanced oil recovery. Thermal-enhanced oil-recovery techniques seek to improve recovery by heating the oil within the formations. When an oil is heated its viscosity will decrease, allowing the oil to flow more easily towards the production wells. The heat can either be generated at the surface and then injected underground in the form of steam, or within the formation itself by burning a portion of the oil in place. In situ combustion processes have been successful in producing oil by the partial combustion of the heavy ends of the oil, which are less likely to be recovered. Today, carbon-dioxide injection dominates the new EOR projects coming on stream. When carbon dioxide is dissolved in oil, the viscosity of the oil will drop significantly, making the oil more mobile. If carbon dioxide can be injected under the right set of conditions then the amount of oil produced will increase significantly. This chapter also reviews other EOR techniques including polymer injection, surfactant injection and alkaline injection. The use of micro-organisms that will thrive underground is also described. Finally, we consider the exploitation of the Weyburn Field in Canada. By progressively applying different techniques the field continues to produce oil after more than 50 years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.030 |
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".