A Follow-Up Recovery Method After Cold Heavy Oil Production Cyclic CO2 Injection
Bibliographic record
Abstract
Abstract Cold heavy oil production with sand (CHOPS) is widely used as primary recovery method for heavy oil in western Canada. This process involves sand production in massive amounts. Sand production creates high permeability zones (wormholes) which extend the drainage radius. Typically 5–10% of the OOIP is recovered by this process. Therefore, the need to find a follow-up process is paramount. The objective of this work was to experimentally evaluate the potential of using cyclic CO2 injection for recovering additional oil from depleted foamy oil reservoirs. A total of five depletion tests were conducted in a two meters long sandpack kept in a vertical orientation. The primary depletions at different depletion rates were followed by one or two huff-n-puff cycles of CO2 injection. The total recovery factor after cyclic CO2 injection reached 30% indicating the potential of solvent injection as a secondary oil recovery method. Interestingly, the recovery after the cyclic CO2 injection was more or less independent of depletion rate used in the primary production. It was found that the cyclic CO2 injection was more efficient when the primary depletion was at slow rate and resulted in lower primary depletion recovery. The results of this study show that it may be possible to re-energize the depleted heavy oil reservoirs by injecting CO2, especially those that did not give high recovery factors during the primary depletion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".