An Extensive Review on the Effective Sequence of Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Different enhanced oil recovery (EOR) techniques for heavy oil reservoirs were reviewed for their ranges of applicability using available reports and publications. EOR screening criteria found in the literature are reprinted and provided. After reviewing more than 100 papers on the subject, it is apparent that there is a definitive knowledge gap on the effective sequence of EOR recovery strategies. While there are numerous studies on the application of heavy oil recovery techniques, there is a lack of comparison and categorization of the results. For Canadian reservoirs, the first recovery method that is implemented first is either waterflooding, cold production or in some cases steamflooding. Chemical flooding and other emerging technologies are mostly coupled with these methods. In most reports, conversion of producers to injectors and introducing line drive and edge drive will improve the waterflooding performance. However, coupling waterflooding with horizontal wells, the addition of water mobility control agents and steam stimulation did not improve the waterflooding performance in some cases. In the case of fractured limestone reservoirs, it seems that immiscible gas injection is a suitable EOR method to implement, but because of the reservoir complexity, a clear understanding of the recovery mechanism and reservoir geology is needed. In-situ combustion and steamflooding are among the most efficient heavy oil recovery methods with a large range of applicability, and next to waterflooding, can become the most widely used heavy oil recovery method. Fireflooding methods can be more profitable if they are coupled with simultaneous or intermittent water injection with air. The results obtained from this paper not only will help the petroleum industry to apply each technique to the right candidate fields, but also it will prevent researchers from duplicating unsuccessful research projects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".